Lifelong Machine Learning Research Group

Sorted by Type or Topic

2022

Wang, B., Mendez, J., Shui, C., Zhou, F., Wu, D., Gagné, C., & Eaton, E. (2022). Gap Minimization for Knowledge Sharing and Transfer. ArXiv:2201.11231 Preprint.

@article{Wang2022Gap,
  author = {Wang, Boyu and Mendez, Jorge and Shui, Changjian and Zhou, Fan and Wu, Di and Gagné, Christian and Eaton, Eric},
  year = {2022},
  month = jan,
  title = {Gap Minimization for Knowledge Sharing and Transfer},
  journal = {arXiv:2201.11231 Preprint},
  group = {preprints},
  link_pdf = {https://arxiv.org/pdf/2201.11231.pdf},
  area = {LLMTL}
}

   Learning from multiple related tasks by knowledge sharing and transfer has become
   increasingly relevant over the last two decades. In order to successfully transfer
   information from one task to another, it is critical to understand the similarities
   and differences between the domains. In this paper, we introduce the notion of
   performance gap, an intuitive and novel measure of the distance between
   learning tasks. Unlike existing measures which are used as tools to bound the
   difference of expected risks between tasks (e.g., -divergence or discrepancy
   distance), we theoretically show that the performance gap can be viewed as 
   a data- and algorithm-dependent regularizer, which controls the model complexity
   and leads to finer guarantees. More importantly, it also provides new insights
   and motivates a novel principle for designing strategies for knowledge sharing
   and transfer: gap minimization. We instantiate this principle with two
   algorithms: 1. gapBoost, a novel and principled boosting algorithm that
   explicitly minimizes the performance gap between source and target domains
   for transfer learning; and 2. gapMTNN, a representation learning algorithm
   that reformulates gap minimization as semantic conditional matching for multitask
   learning. Our extensive evaluation on both transfer learning and multitask learning
   benchmark data sets shows that our methods outperform existing baselines.

Vogelstein, J. T., Verstynen, T., Kording, K. P., Isik, L., Krakauer, J. W., Etienne-Cummings, R., Ogburn, E. L., Priebe, C. E., Burns, R., Kutten, K., Knierim, J. J., Potash, J. B., Hartung, T., Smirnova, L., Worley, P., Savonenko, A., Phillips, I., Miller, M. I., Vidal, R., … Yang, W. (2022). Prospective Learning: Back to the Future. ArXiv:2201.07372 Preprint.

@article{Vogelstein2022Prospective,
  author = {Vogelstein, Joshua T. and Verstynen, Timothy and Kording, Konrad P. and Isik, Leyla and Krakauer, John W. and Etienne-Cummings, Ralph and Ogburn, Elizabeth L. and Priebe, Carey E. and Burns, Randal and Kutten, Kwame and Knierim, James J. and Potash, James B. and Hartung, Thomas and Smirnova, Lena and Worley, Paul and Savonenko, Alena and Phillips, Ian and Miller, Michael I. and Vidal, Rene and Sulam, Jeremias and Charles, Adam and Cowan, Noah J. and Bichuch, Maxim and Venkataraman, Archana and Li, Chen and Thakor, Nitish and Kebschull, Justus M and Albert, Marilyn and Xu, Jinchong and Shuler, Marshall Hussain and Caffo, Brian and Ratnanather, Tilak and Geisa, Ali and Roh, Seung-Eon and Yezerets, Eva and Madhyastha, Meghana and How, Javier J. and Tomita, Tyler M. and Dey, Jayanta and Ningyuan and Huang and Shin, Jong M. and Kinfu, Kaleab Alemayehu and Chaudhari, Pratik and Baker, Ben and Schapiro, Anna and Jayaraman, Dinesh and Eaton, Eric and Platt, Michael and Ungar, Lyle and Wehbe, Leila and Kepecs, Adam and Christensen, Amy and Osuagwu, Onyema and Brunton, Bing and Mensh, Brett and Muotri, Alysson R. and Silva, Gabriel and Puppo, Francesca and Engert, Florian and Hillman, Elizabeth and Brown, Julia and White, Chris and Yang, Weiwei},
  year = {2022},
  month = jan,
  title = {Prospective Learning: Back to the Future},
  journal = {arXiv:2201.07372 Preprint},
  group = {preprints},
  link_pdf = {https://arxiv.org/pdf/2201.07372.pdf},
  area = {LLMTL}
}

   Research on both natural intelligence (NI) and artificial intelligence (AI)
   generally assumes that the future resembles the past: intelligent agents
   or systems (what we call ’intelligence’) observe and act on the world, then
   use this experience to act on future experiences of the same kind. We call
   this ’retrospective learning’. For example, an intelligence may see a set of
   pictures of objects, along with their names, and learn to name them.
   A retrospective learning intelligence would merely be able to name 
   more pictures of the same objects. We argue that this is not what true intelligence
   is about. In many real world problems, both NIs and AIs will have to learn
   for an uncertain future. Both must update their internal models to be useful
   for future tasks, such as naming fundamentally new objects and using
   these objects effectively in a new context or to achieve previously
   unencountered goals. This ability to learn for the future we call
   ’prospective learning’. We articulate four relevant factors that jointly
   define prospective learning. Continual learning enables intelligences
   to remember those aspects of the past which it believes will be most useful
   in the future. Prospective constraints (including biases and priors)
   facilitate the intelligence finding general solutions that will be
   applicable to future problems. Curiosity motivates taking actions that
   inform future decision making, including in previously unmet situations.
   Causal estimation enables learning the structure of relations that guide
   choosing actions for specific outcomes, even when the specific action-outcome
   contingencies have never been observed before. We argue that a paradigm shift
   from retrospective to prospective learning will enable the communities
   that study intelligence to unite and overcome existing bottlenecks
   to more effectively explain, augment, and engineer intelligences.

2021

Mendez, J., & Eaton, E. (2021). Lifelong learning of compositional structures. International Conference on Learning Representations.

@inproceedings{Mendez2021Lifelong,
  title = {Lifelong learning of compositional structures},
  author = {Mendez, Jorge and Eaton, Eric},
  booktitle = {International Conference on Learning Representations},
  year = {2021},
  group = {journals},
  preprint = {Mendez2021Lifelong.pdf},
  link = {https://openreview.net/forum?id=ADWd4TJO13G},
  link_video = {https://iclr.cc/virtual/2021/poster/2733},
  code = {https://github.com/Lifelong-ML/Mendez2020Compositional},
  area = {LLMTL},
  funding = {DARPA, ARO}
}

    A hallmark of human intelligence is the ability to construct 
    self-contained chunks of knowledge and adequately reuse them 
    in novel combinations for solving different yet structurally 
    related problems. Learning such compositional structures has 
    been a significant challenge for artificial systems, due to 
    the combinatorial nature of the underlying search problem. 
    To date, research into compositional learning has largely 
    proceeded separately from work on lifelong or continual 
    learning. We integrate these two lines of work to present a 
    general-purpose framework for lifelong learning of 
    compositional structures that can be used for solving a 
    stream of related tasks. Our framework separates the learning 
    process into two broad stages: learning how to best combine 
    existing components in order to assimilate a novel problem, 
    and learning how to adapt the set of existing components to 
    accommodate the new problem. This separation explicitly 
    handles the trade-off between the stability required to 
    remember how to solve earlier tasks and the flexibility
    required to solve new tasks, as we show empirically in an 
    extensive evaluation.

Lee, S., Behpour, S., & Eaton, E. (2021). Sharing less is more: Lifelong learning in deep networks with selective layer transfer. Proceedings of the 38th International Conference on Machine Learning (ICML-21).

@inproceedings{Lee2021Sharing,
  author = {Lee, Seungwon and Behpour, Sima and Eaton, Eric},
  year = {2021},
  title = {Sharing less is more: Lifelong learning in deep networks with selective layer transfer},
  booktitle = {Proceedings of the 38th International Conference on Machine Learning (ICML-21)},
  group = {journals},
  preprint = {Lee2021Sharing.pdf},
  supplement = {Lee2021Sharing-Supplement.pdf},
  slides = {Lee2021Sharing-Slides.pdf},
  link = {https://proceedings.mlr.press/v139/lee21a.html},
  link_video = {https://icml.cc/virtual/2021/poster/10559},
  code = {https://github.com/Lifelong-ML/LASEM},
  area = {LLMTL},
  funding = {DARPA, ARO}
}

   Effective lifelong learning across diverse tasks requires
   the transfer of diverse knowledge, yet transferring irrelevant
   knowledge may lead to interference and catastrophic forgetting.
   In deep networks, transferring the appropriate granularity of
   knowledge is as important as the transfer mechanism, and must be
   driven by the relationships among tasks. We first show that
   the lifelong learning performance of several current 
   deep learning architectures can be significantly improved by 
   transfer at the appropriate layers. We then develop 
   an expectation-maximization (EM) method to automatically select
   the appropriate transfer configuration and optimize the task
   network weights. This EM-based selective transfer is highly
   effective, balancing transfer performance on all tasks with
   avoiding catastrophic forgetting, as demonstrated on three
   algorithms in several lifelong object classification scenarios.

Journal Articles

Vedder, K., & Eaton, E. (2021). Sparse PointPillars: Maintaining and Exploiting Input Sparsity to Improve Runtime on Embedded Systems. ArXiv:2106.06882 Preprint.

@article{Vedder2021Sparse,
  author = {Vedder, Kyle and Eaton, Eric},
  year = {2021},
  title = {Sparse PointPillars: Maintaining and Exploiting Input Sparsity to Improve Runtime on Embedded Systems},
  journal = {arXiv:2106.06882 Preprint},
  group = {preprints},
  link_pdf = {https://arxiv.org/pdf/2106.06882},
  area = {Robotics}
}

   Bird’s Eye View (BEV) is a popular representation for processing 3D point 
   clouds, and by its nature is fundamentally sparse. Motivated by the 
   computational limitations of mobile robot platforms, we take a fast, 
   high-performance BEV 3D object detector - PointPillars - and modify its 
   backbone to maintain and exploit this input sparsity, leading to decreased 
   runtimes. We present results on KITTI, a canonical 3D detection dataset, 
   and Matterport-Chair, a novel Matterport3D-derived chair detection dataset 
   from scenes in real furnished homes. We evaluate runtime characteristics 
   using a desktop GPU, an embedded ML accelerator, and a robot CPU, 
   demonstrating that our method results in significant runtime decreases 
   (2x or more) for embedded systems with only a modest decrease in detection 
   quality. Our work represents a new approach for practitioners to optimize 
   models for embedded systems by maintaining and exploiting input sparsity 
   throughout their entire pipeline to reduce runtime and resource usage 
   while preserving detection performance. All models, weights, experimental 
   configurations, and datasets used are publicly available.

Geisa, A., Mehta, R., Helm, H. S., Dey, J., Eaton, E., Dick, J., Priebe, C. E., & Vogelstein, J. T. (2021). Towards a theory of out-of-distribution learning. ArXiv:2109.14501 Preprint.

@article{Geisa2021Towards,
  author = {Geisa, Ali and Mehta, Ronak and Helm, Hayden S. and Dey, Jayanta and Eaton, Eric and Dick, Jeffery and Priebe, Carey E. and Vogelstein, Joshua T.},
  year = {2021},
  title = {Towards a theory of out-of-distribution learning},
  journal = {arXiv:2109.14501 Preprint},
  group = {preprints},
  link_pdf = {https://arxiv.org/pdf/2109.14501.pdf},
  area = {OOD}
}

   What is learning? 20st century formalizations of learning theory
   – which precipitated revolutions in artificial intelligence
   – focus primarily on in−distribution learning, that is, learning
   under the assumption that the training data are sampled from the same
   distribution as the evaluation distribution. This assumption renders
   these theories inadequate for characterizing 21st century
   real world data problems, which are typically characterized by evaluation
   distributions that differ from the training data distributions
   (referred to as out-of-distribution learning). We therefore make a small
   change to existing formal definitions of learnability by relaxing
   that assumption. We then introduce learning efficiency (LE) to quantify
   the amount a learner is able to leverage data for a given problem,
   regardless of whether it is an in- or out-of-distribution problem.
   We then define and prove the relationship between generalized notions of
   learnability, and show how this framework is sufficiently general 
   to characterize transfer, multitask, meta, continual, and lifelong learning.
   We hope this unification helps bridge the gap between empirical practice and
   theoretical guidance in real world problems. Finally, because biological learning
   continues to outperform machine learning algorithms on certain OOD challenges,
   we discuss the limitations of this framework vis-á-vis its ability to formalize
   biological learning, suggesting multiple avenues for future research.

2020

Mendez, J., & Eaton, E. (2020). A general framework for continual learning of compositional structures. Continual Learning Workshop at ICML.

@inproceedings{Mendez2020General,
  title = {A general framework for continual learning of compositional structures},
  author = {Mendez, Jorge and Eaton, Eric},
  booktitle = {Continual Learning Workshop at ICML},
  year = {2020},
  note = {Superseded by the ICLR-21 paper: Lifelong learning of compositional structures.},
  group = {refereedworkshop},
  preprint = {Mendez2020General.pdf},
  supplement = {Mendez2020General-supplement.pdf},
  area = {LLMTL},
  funding = {DARPA}
}

    A hallmark of human intelligence is the ability to construct self-contained chunks of
    knowledge and adequately reuse them in novel combinations for solving different
    yet structurally related problems. Learning such compositional structures has been
    a significant challenge for artificial systems, due to the combinatorial nature of
    the underlying search problem. To date, research into compositional learning
    has largely proceeded separately from work on lifelong or continual learning.
    We integrate these two lines of work to present a general-purpose framework for
    lifelong learning of compositional structures that can be used for solving a stream of
    related tasks. Our framework separates the learning process into two broad stages:
    learning how to best combine existing components in order to assimilate a novel
    problem, and learning how to adapt the set of existing components to accommodate
    the new problem. This separation explicitly handles the trade-off between the
    stability required to remember how to solve earlier tasks and the flexibility required
    to solve new tasks, as we show empirically in an extensive evaluation.

Gennatas, E. D., Friedman, J. H., Ungar, L. H., Pirracchio, R., Eaton, E., Reichmann, L. G., Interian, Y., Luna, J. M., Simone, C. B., Auerbach, A., Delgado, E., van der Laan, M. J., Solberg, T. D., & Valdes, G. (2020). Expert-augmented machine learning. Proceedings of the National Academy of Sciences, 117(9), 4571–4577.

@inproceedings{Gennatas2020ExpertAugmented,
  author = {Gennatas, Efstathios D. and Friedman, Jerome H. and Ungar, Lyle H. and Pirracchio, Romain and Eaton, Eric and Reichmann, Lara G. and Interian, Yannet and Luna, Jose Marcio and Simone, Charles B. and Auerbach, Andrew and Delgado, Elier and van der Laan, Mark J. and Solberg, Timothy D. and Valdes, Gilmer},
  title = {Expert-augmented machine learning},
  volume = {117},
  number = {9},
  pages = {4571--4577},
  year = {2020},
  publisher = {National Academy of Sciences},
  journal = {Proceedings of the National Academy of Sciences},
  group = {journals},
  link = {https://www.pnas.org/content/117/9/4571},
  link_pdf = {https://www.pnas.org/content/117/9/4571.full.pdf},
  link_supplement = {https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1906831117/-/DCSupplemental},
  area = {Medicine, InteractiveLearning}
}

	Machine learning is increasingly used across fields to derive insights from data, 
	which further our understanding of the world and help us anticipate the future. The 
	performance of predictive modeling is dependent on the amount and quality of available 
	data. In practice, we rely on human experts to perform certain tasks and on machine 
	learning for others. However, the optimal learning strategy may involve combining the 
	complementary strengths of humans and machines. We present expert-augmented machine 
	learning, an automated way to automatically extract problem-specific human expert 
	knowledge and integrate it with machine learning to build robust, dependable, and 
	data-efficient predictive models.Machine learning is proving invaluable across 
	disciplines. However, its success is often limited by the quality and quantity of 
	available data, while its adoption is limited by the level of trust afforded by given 
	models. Human vs. machine performance is commonly compared empirically to decide 
	whether a certain task should be performed by a computer or an expert. In reality, the 
	optimal learning strategy may involve combining the complementary strengths of humans 
	and machines. Here, we present expert-augmented machine learning (EAML), an automated 
	method that guides the extraction of expert knowledge and its integration into 
	machine-learned models. We used a large dataset of intensive-care patient data to 
	derive 126 decision rules that predict hospital mortality. Using an online platform, 
	we asked 15 clinicians to assess the relative risk of the subpopulation defined by 
	each rule compared to the total sample. We compared the clinician-assessed risk to the 
	empirical risk and found that, while clinicians agreed with the data in most cases, 
	there were notable exceptions where they overestimated or underestimated the true 
	risk. Studying the rules with greatest disagreement, we identified problems with the 
	training data, including one miscoded variable and one hidden confounder. Filtering 
	the rules based on the extent of disagreement between clinician-assessed risk and 
	empirical risk, we improved performance on out-of-sample data and were able to train 
	with less data. EAML provides a platform for automated creation of problem-specific 
	priors, which help build robust and dependable machine-learning models in critical 
	applications.

Mendez, J., & Eaton, E. (2020). Lifelong learning of factored policies via policy gradients. 4th Lifelong Learning Workshop at ICML.

@inproceedings{Mendez2020Lifelong,
  title = {Lifelong learning of factored policies via policy gradients},
  author = {Mendez, Jorge and Eaton, Eric},
  booktitle = {4th Lifelong Learning Workshop at ICML},
  year = {2020},
  note = {Awarded Best Paper at the workshop; superseded by the NeurIPS-20 version.},
  group = {refereedworkshop},
  preprint = {Mendez2020Lifelong.pdf},
  link = {https://proceedings.neurips.cc/paper/2020/hash/a58149d355f02887dfbe55ebb2b64ba3-Abstract.html},
  area = {LLMTL},
  funding = {DARPA}
}

    Policy gradient methods have shown success in
    learning continuous control policies for highdimensional
    dynamical systems. A major downside
    of such methods is the amount of exploration
    they require before yielding high-performing policies.
    In a lifelong learning setting, in which an
    agent is faced with multiple consecutive tasks over
    its lifetime, reusing information from previously
    seen tasks can substantially accelerate the learning
    of new tasks. We provide a novel method for
    lifelong policy gradient learning that trains lifelong
    function approximators directly via policy
    gradients, allowing the agent to benefit from accumulated
    knowledge throughout the entire training
    process. We show empirically that our algorithm
    learns faster and converges to better policies than
    single-task and lifelong learning baselines, and
    completely avoids catastrophic forgetting on a
    variety of challenging domains.

Mendez, J., Wang, B., & Eaton, E. (2020). Lifelong policy gradient learning of factored policies for faster training without forgetting. Advances in Neural Information Processing Systems.

@inproceedings{Mendez2020LifelongPG,
  title = {Lifelong policy gradient learning of factored policies for faster training without forgetting},
  author = {Mendez, Jorge and Wang, Boyu and Eaton, Eric},
  booktitle = {Advances in Neural Information Processing Systems},
  year = {2020},
  note = {Earlier version was awarded best paper at the ICML'20 Workshop on Lifelong Learning},
  group = {journals},
  preprint = {Mendez2020LifelongPG.pdf},
  link_supplement = {https://proceedings.neurips.cc/paper/2020/file/a58149d355f02887dfbe55ebb2b64ba3-Supplemental.pdf},
  code = {https://github.com/Lifelong-ML/LPG-FTW},
  video = {https://neurips.cc/virtual/2020/protected/poster_a58149d355f02887dfbe55ebb2b64ba3.html},
  area = {LLMTL},
  funding = {DARPA}
}

    Policy gradient methods have shown success in learning control 
    policies for highdimensional dynamical systems. Their biggest 
    downside is the amount of exploration they require before 
    yielding high-performing policies. In a lifelong learning 
    setting, in which an agent is faced with multiple consecutive 
    tasks over its lifetime, reusing information from previously 
    seen tasks can substantially accelerate the learning of new 
    tasks. We provide a novel method for lifelong policy gradient 
    learning that trains lifelong function approximators directly 
    via policy gradients, allowing the agent to benefit from 
    accumulated knowledge throughout the entire training process. 
    We show empirically that our algorithm learns faster and 
    converges to better policies than single-task and lifelong 
    learning baselines, and completely avoids catastrophic forgetting 
    on a variety of challenging domains.

Lee, S., Behpour, S., & Eaton, E. (2020). Sharing less is more: Lifelong learning in deep networks with selective layer transfer. 4th Lifelong Learning Workshop at ICML.

@inproceedings{Lee2020Sharing,
  title = {Sharing less is more: Lifelong learning in deep networks with selective layer transfer},
  author = {Lee, Seungwon and Behpour, Sima and Eaton, Eric},
  booktitle = {4th Lifelong Learning Workshop at ICML},
  year = {2020},
  group = {refereedworkshop},
  preprint = {Lee2020Sharing.pdf},
  area = {LLMTL},
  funding = {DARPA}
}

    Effective lifelong learning across diverse tasks
    requires diverse knowledge, yet transferring irrelevant
    knowledge may lead to interference and
    catastrophic forgetting. In deep networks, transferring
    the appropriate granularity of knowledge
    is as important as the transfer mechanism, and
    must be driven by the relationships among tasks.
    We first show that the lifelong learning performance
    of several current deep learning architectures
    can be significantly improved by transfer
    at the appropriate layers. We then develop an
    expectation-maximization (EM) method to automatically
    select the appropriate transfer configuration
    and optimize the task network weights. This
    EM-based selective transfer is highly effective,
    as demonstrated on three algorithms in several
    lifelong object classification scenarios.

Rostami, M., Isele, D., & Eaton, E. (2020). Using task descriptions in lifelong machine learning for improved performance and zero-shot transfer. Journal of Artificial Intelligence Research, 67, 673–704.

@inproceedings{Rostami2020Using,
  title = {Using task descriptions in lifelong machine learning for improved performance and zero-shot transfer},
  author = {Rostami, Mohammad and Isele, David and Eaton, Eric},
  journal = {Journal of Artificial Intelligence Research},
  volume = {67},
  pages = {673--704},
  year = {2020},
  group = {journals},
  preprint = {Rostami2020Using.pdf},
  link = {http://doi.org/10.1613/jair.1.11304},
  area = {LLMTL},
  funding = {DARPA}
}

    Knowledge transfer between tasks can improve the performance of learned models, but 
    requires an accurate estimate of inter-task relationships to identify the relevant 
    knowledge to transfer. These inter-task relationships are typically estimated based 
    on training data for each task, which is inefficient in lifelong learning settings 
    where the goal is to learn each consecutive task rapidly from as little data as 
    possible. To reduce this burden, we develop a lifelong learning method based on 
    coupled dictionary learning that utilizes high-level task descriptions to model 
    inter-task relationships. We show that using task descriptors improves the performance 
    of the learned task policies, providing both theoretical justification for the benefit 
    and empirical demonstration of the improvement across a variety of learning problems. 
    Given only the descriptor for a new task, the lifelong learner is also able to 
    accurately predict a model for the new task through zero-shot learning using the 
    coupled dictionary, eliminating the need to gather training data before addressing the 
    task.

2019

Eaton, E. (2019). A lightweight approach to academic research group management using online tools: Spend more time on research and less on management. Proceedings of the Educational Advances in Artificial Intelligene (EAAI) Symposium, 9644–9647.

@inproceedings{Eaton2019Lightweight,
  title = {A lightweight approach to academic research group management using online tools: Spend more time on research and less on management},
  author = {Eaton, Eric},
  booktitle = {Proceedings of the Educational Advances in Artificial Intelligene (EAAI) Symposium},
  pages = {9644--9647},
  year = {2019},
  group = {refereedworkshop},
  preprint = {Eaton2019Lightweight.pdf},
  link = {http://doi.org/10.1609/aaai.v33i01.33019644},
  area = {Education}
}

    After years of taking a trial-and-error approach to managing a moderate-size academic 
    research group, I settled on using a set of online tools and protocols that seem 
    effective, require relatively little effort to use and maintain, and are inexpensive. 
    This paper discusses this approach to communication, project management, document and 
    code management, and logistics. It is my hope that other researchers, especially new 
    faculty and research scientists, might find this set of tools and protocols useful 
    when determining how to manage their own research group. This paper is targeted toward 
    research groups based in mathematics and engineering, although faculty in other 
    disciplines may find inspiration in some of these ideas.

Reid, J. E., & Eaton, E. (2019). Artificial intelligence for pediatric ophthalmology. Current Opinion in Ophthalmology, 30(5), 337–346.

@inproceedings{Reid2019Artificial,
  title = {Artificial intelligence for pediatric ophthalmology},
  author = {Reid, Julia E. and Eaton, Eric},
  journal = {Current Opinion in Ophthalmology},
  volume = {30},
  number = {5},
  pages = {337-346},
  bib2html_dl_html = {http://doi.org/10.1097/ICU.0000000000000593},
  year = {2019},
  group = {journals},
  preprint = {Reid2019Artificial.pdf},
  area = {Medicine},
  funding = {DARPA}
}

    PURPOSE OF REVIEW 
    Despite the impressive results of recent artificial intelligence applications to 
    general ophthalmology, comparatively less progress has been made toward solving 
    problems in pediatric ophthalmology using similar techniques. This article discusses 
    the unique needs of pediatric patients and how artificial intelligence techniques can 
    address these challenges, surveys recent applications to pediatric ophthalmology, and 
    discusses future directions.

    RECENT FINDINGS 
    The most significant advances involve the automated detection of retinopathy of 
    prematurity, yielding results that rival experts. Machine learning has also been 
    applied to the classification of pediatric cataracts, prediction of postoperative 
    complications following cataract surgery, detection of strabismus and refractive 
    error, prediction of future high myopia, and diagnosis of reading disability. In 
    addition, machine learning techniques have been used for the study of visual 
    development, vessel segmentation in pediatric fundus images, and ophthalmic image 
    synthesis.

    SUMMARY 
    Artificial intelligence applications could significantly benefit clinical care by 
    optimizing disease detection and grading, broadening access to care, furthering 
    scientific discovery, and improving clinical efficiency. These methods need to match 
    or surpass physician performance in clinical trials before deployment with patients. 
    Owing to the widespread use of closed-access data sets and software implementations, 
    it is difficult to directly compare the performance of these approaches, and 
    reproducibility is poor. Open-access data sets and software could alleviate these 
    issues and encourage further applications to pediatric ophthalmology.

Luna, J. M., Gennatas, E. D., Ungar, L. H., Eaton, E., Diffenderfer, E. S., Jensen, S. T., Simone, C. B., Friedman, J. H., Solberg, T. D., & Valdes, G. (2019). Building more accurate decision trees with the additive tree. Proceedings of the National Academy of Sciences, 116(40), 19887–19893.

@inproceedings{Luna2019Building,
  author = {Luna, Jose Marcio and Gennatas, Efstathios D. and Ungar, Lyle H. and Eaton, Eric and Diffenderfer, Eric S. and Jensen, Shane T. and Simone, Charles B. and Friedman, Jerome H. and Solberg, Timothy D. and Valdes, Gilmer},
  title = {Building more accurate decision trees with the additive tree},
  volume = {116},
  number = {40},
  pages = {19887--19893},
  year = {2019},
  publisher = {National Academy of Sciences},
  journal = {Proceedings of the National Academy of Sciences},
  group = {journals},
  link = {https://www.pnas.org/content/116/40/19887},
  link_pdf = {https://www.pnas.org/content/pnas/116/40/19887.full.pdf},
  link_supplement = {https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1816748116/-/DCSupplemental},
  area = {Medicine, InteractiveLearning}
}

    As machine learning applications expand to high-stakes areas such as criminal justice, 
    finance, and medicine, legitimate concerns emerge about high-impact effects of 
    individual mispredictions on people’s lives. As a result, there has 
    been increasing interest in understanding general machine learning models to overcome 
    possible serious risks. Current decision trees, such as Classification and Regression 
    Trees (CART), have played a predominant role in fields such as medicine, due to their 
    simplicity and intuitive interpretation. However, such trees suffer from intrinsic 
    limitations in predictive power. We developed the additive tree, a theoretical 
    approach to generate a more accurate and interpretable decision tree, which reveals 
    connections between CART and gradient boosting. The additive tree exhibits superior 
    predictive performance to CART, as validated on 83 classification tasks.The expansion 
    of machine learning to high-stakes application domains such as medicine, finance, and 
    criminal justice, where making informed decisions requires clear understanding of the 
    model, has increased the interest in interpretable machine learning. The widely used 
    Classification and Regression Trees (CART) have played a major role in health 
    sciences, due to their simple and intuitive explanation of predictions. Ensemble 
    methods like gradient boosting can improve the accuracy of decision trees, but at the 
    expense of the interpretability of the generated model. Additive models, such as those 
    produced by gradient boosting, and full interaction models, such as CART, have been 
    investigated largely in isolation. We show that these models exist along a spectrum, 
    revealing previously unseen connections between these approaches. This paper 
    introduces a rigorous formalization for the additive tree, an empirically validated 
    learning technique for creating a single decision tree, and shows that this method can 
    produce models equivalent to CART or gradient boosted stumps at the extremes by 
    varying a single parameter. Although the additive tree is designed primarily to 
    provide both the model interpretability and predictive performance needed for 
    high-stakes applications like medicine, it also can produce decision trees represented 
    by hybrid models between CART and boosted stumps that can outperform either of these 
    approaches.

Rostami, M., Kolouri, S., Eaton, E., & Kim, K. (2019). Deep transfer learning for few-shot SAR image classification. Remote Sensing, 11, 1374.

@inproceedings{Rostami2019Deep,
  title = {Deep transfer learning for few-shot SAR image classification},
  author = {Rostami, Mohammad and Kolouri, Soheil and Eaton, Eric and Kim, Kyungnam},
  journal = {Remote Sensing},
  volume = {11},
  pages = {1374},
  year = {2019},
  group = {journals},
  preprint = {Rostami2019Deep.pdf},
  link = {http://doi.org/10.3390/rs11111374},
  area = {TransferLearning},
  funding = {DARPA}
}

The emergence of Deep Neural Networks (DNNs) has lead to high-performance
supervised learning algorithms for the Electro-Optical (EO) domain classification and detection
problems. This success is because generating huge labeled datasets has become possible using
modern crowdsourcing labeling platforms such as Amazon’s Mechanical Turk that recruit ordinary
people to label data. Unlike the EO domain, labeling the Synthetic Aperture Radar (SAR) domain data
can be much more challenging, and for various reasons, using crowdsourcing platforms is not feasible
for labeling the SAR domain data. As a result, training deep networks using supervised learning is
more challenging in the SAR domain. In the paper, we present a new framework to train a deep neural
network for classifying Synthetic Aperture Radar (SAR) images by eliminating the need for a huge
labeled dataset. Our idea is based on transferring knowledge from a related EO domain problem,
where labeled data are easy to obtain. We transfer knowledge from the EO domain through learning
a shared invariant cross-domain embedding space that is also discriminative for classification. To this
end, we train two deep encoders that are coupled through their last year to map data points from
the EO and the SAR domains to the shared embedding space such that the distance between the
distributions of the two domains is minimized in the latent embedding space. We use the Sliced
Wasserstein Distance (SWD) to measure and minimize the distance between these two distributions
and use a limited number of SAR label data points to match the distributions class-conditionally. As a
result of this training procedure, a classifier trained from the embedding space to the label space using
mostly the EO data would generalize well on the SAR domain. We provide a theoretical analysis
to demonstrate why our approach is effective and validate our algorithm on the problem of ship
classification in the SAR domain by comparing against several other competing learning approaches.

Lee, S., Stokes, J., & Eaton, E. (2019). Learning shared knowledge for deep lifelong learning using deconvolutional networks. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19), 2837–2844.

@inproceedings{Lee2019Learning,
  title = {Learning shared knowledge for deep lifelong learning using deconvolutional networks},
  author = {Lee, Seungwon and Stokes, James and Eaton, Eric},
  booktitle = {Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19)},
  pages = {2837--2844},
  year = {2019},
  month = jul,
  group = {journals},
  preprint = {Lee2019Learning.pdf},
  link = {http://doi.org/10.24963/ijcai.2019/393},
  code = {https://github.com/Lifelong-ML/DF-CNN},
  area = {LLMTL},
  funding = {DARPA, AFOSR}
}

    Current mechanisms for knowledge transfer in deep networks tend to either share the 
    lower layers between tasks, or build upon representations trained on other tasks. 
    However, existing work in non-deep multi-task and lifelong learning has shown success 
    with using factorized representations of the model parameter space for transfer, 
    permitting more flexible construction of task models. Inspired by this idea, we 
    introduce a novel architecture for sharing latent factorized representations in 
    convolutional neural networks (CNNs). The proposed approach, called a deconvolutional 
    factorized CNN, uses a combination of deconvolutional factorization and tensor 
    contraction to perform flexible transfer between tasks. Experiments on two computer 
    vision data sets show that the DF-CNN achieves superior performance in challenging 
    lifelong learning settings, resists catastrophic forgetting, and exhibits reverse 
    transfer to improve previously learned tasks from subsequent experience without 
    retraining.

Shen, P., Braham, W., Yi, Y. K., & Eaton, E. (2019). Rapid multi-objective optimization with multi-year future weather condition and decision-making support for building retrofit. Energy, 172, 892–912.

@inproceedings{Shen2019Rapid,
  title = {Rapid multi-objective optimization with multi-year future weather condition and decision-making support for building retrofit},
  keywords = {Building retrofit, Climate change, Heuristic method, Hierarchical clustering, Optimization, Pareto fronts},
  author = {Shen, Pengyuan and Braham, William and Yi, {Yun Kyu} and Eaton, Eric},
  year = {2019},
  month = apr,
  volume = {172},
  pages = {892--912},
  journal = {Energy},
  publisher = {Elsevier Limited},
  group = {journals},
  link = {http://doi.org/10.1016/j.energy.2019.01.164},
  area = {CompSus}
}

   A method of fast multi-objective optimization and decision-making support for building 
   retrofit planning is developed, and lifecycle cost analysis method taking into account 
   of future climate condition is used in evaluating the retrofit performance. In order to 
   resolve the optimization problem in a fast manner with recourse to non-dominate sorting 
   differential evolution algorithm, the simplified hourly dynamic simulation modeling 
   tool SimBldPy is used as the simulator for objective function evaluation. Moreover, the 
   generated non-dominated solutions are treated and rendered by a layered scheme using 
   agglomerative hierarchical clustering technique to make it more intuitive and sense 
   making during the decision-making process as well as to be better presented. The 
   suggested optimization method is implemented to the retrofit planning of a campus 
   building in UPenn with various energy conservation measures (ECM) and costs, and more 
   than one thousand Pareto fronts are obtained and being analyzed according to the 
   proposed decision-making framework. Twenty ECM combinations are eventually selected 
   from all generated Pareto fronts. It is manifested that the developed decision-making 
   support scheme shows robustness in dealing with retrofit optimization problem and is 
   able to provide support for brainstorming and enumerating various possibilities during 
   the decision-making process.

Rostami, M., Kolouri, S., Eaton, E., & Kim, K. (2019, June). SAR image classification using few-shot cross-domain transfer learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops.

@inproceedings{Rostami2019SAR,
  author = {Rostami, Mohammad and Kolouri, Soheil and Eaton, Eric and Kim, Kyungnam},
  title = {SAR image classification using few-shot cross-domain transfer learning},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
  month = jun,
  year = {2019},
  group = {refereedworkshop},
  preprint = {Rostami2019SAR.pdf},
  area = {TransferLearning},
  funding = {DARPA}
}

 Data-driven classification algorithms based on deep convolutional neural networks (CNNs) 
 have reached human-level performance for many tasks within Electro-Optical (EO) computer 
 vision.Despite being the prevailing visual sensory data, EO imaging is not effective in 
 applications such as environmental monitoring at extended periods, where data collection 
 at occluded weather is necessary.Synthetic Aperture Radar (SAR) is an effective imaging 
 tool to circumvent these limitations and collect visual sensory information continually. 
 However, replicating the success of deep learning on SAR domains is not straightforward. 
 This is mainly because training deep networks requires huge labeled datasets anddata 
 labeling is a lot more challenging in SAR domains. We develop an algorithm to transfer 
 knowledge from EO domains to SAR domains to eliminate the need for huge labeled data 
 points in the SAR domains. Our idea is to learn a shared domain-invariant embedding for 
 cross-domain knowledge transfer such that the embedding is discriminative for two related 
 EO and SAR tasks, while the latent data distributions for both domains remain similar. 
 As a result, a classifier learned using mostly EO data can generalize well on the related 
 task for the EO domain.

Wang, B., Mendez, J., Cai, M., & Eaton, E. (2019). Transfer learning via minimizing the performance gap between domains. Advances in Neural Information Processing Systems, 32, 10645–10655.

@inproceedings{Wang2019Transfer,
  title = {Transfer learning via minimizing the performance gap between domains},
  author = {Wang, Boyu and Mendez, Jorge and Cai, Mingbo and Eaton, Eric},
  booktitle = {Advances in Neural Information Processing Systems},
  volume = {32},
  pages = {10645--10655},
  year = {2019},
  group = {journals},
  preprint = {Wang2019Transfer.pdf},
  supplement = {Wang2019Transfer-Supplement.pdf},
  code = {https://github.com/bwang-ml/gapBoost},
  area = {TransferLearning},
  funding = {DARPA, AFOSR}
}

   We propose a new principle for transfer learning, based on a straightforward intuition: 
   if two domains are similar to each other, the model trained on one domain should also 
   perform well on the other domain, and vice versa. To formalize this intuition, we 
   define the performance gap as a measure of the discrepancy between the source and 
   target domains. We derive generalization bounds for the instance weighting approach to 
   transfer learning, showing that the performance gap can be viewed as an 
   algorithm-dependent regularizer, which controls the model complexity. Our theoretical 
   analysis provides new insight into transfer learning and motivates a set of general, 
   principled rules for designing new instance weighting schemes for transfer learning. 
   These rules lead to gapBoost, a novel and principled boosting approach for transfer 
   learning. Our experimental evaluation on benchmark data sets shows that gapBoost 
   significantly outperforms previous boosting-based transfer learning algorithms.

2015 - 2018

Mendez, J. A., Shivkumar, S., & Eaton, E. (2018). Lifelong inverse reinforcement learning. Neural Information Processing Systems.

@inproceedings{Mendez2018Lifelong,
  title = {Lifelong inverse reinforcement learning},
  author = {Mendez, Jorge A. and Shivkumar, Shashank and Eaton, Eric},
  journal = {Neural Information Processing Systems},
  year = {2018},
  group = {journals},
  preprint = {Mendez2018Lifelong.pdf},
  link_supplement = {https://proceedings.neurips.cc/paper/2018/file/2d969e2cee8cfa07ce7ca0bb13c7a36d-Supplemental.zip},
  code = {https://github.com/Lifelong-ML/ELIRL.git},
  video = {https://youtu.be/Of5OyuOrePw},
  poster = {https://www.seas.upenn.edu/~eeaton/papers/Mendez2018Lifelong-poster.pdf},
  area = {LLMTL},
  funding = {DARPA, AFOSR}
}

   Methods for learning from demonstration (LfD) have shown 
   success in acquiring behavior policies by imitating a user. 
   However, even for a single task, LfD may require numerous 
   demonstrations. For versatile agents that must learn many 
   tasks via demonstration, this process would substantially 
   burden the user if each task were learned in isolation. To 
   address this challenge, we introduce the novel problem of 
   lifelong learning from demonstration, which allows the 
   agent to continually build upon knowledge learned from 
   previously demonstrated tasks to accelerate the learning 
   of new tasks, reducing the amount of demonstrations 
   required. As one solution to this problem, we propose the 
   first lifelong learning approach to inverse reinforcement 
   learning, which learns consecutive tasks via demonstration, 
   continually transferring knowledge between tasks to improve 
   performance.

Isele, D., Eaton, E., Roberts, M., & Aha, D. (2018). Modeling consecutive task learning with task graph agendas. Proceedings of the Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-18).
```
@inproceedings{Isele2018Modeling,
  title = {Modeling consecutive task learning with task graph agendas},
  author = {Isele, David and Eaton, Eric and Roberts, Mark and Aha, David},
  booktitle = {Proceedings of the Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-18)},
  year = {2018},
  group = {refereedshort},
  preprint = {Isele2018Modeling.pdf},
  area = {LLMTL},
  funding = {AFOSR}
}
```

Rostami, M., Kolouri, S., Kim, K., & Eaton, E. (2018). Multi-agent distributed lifelong learning for collective knowledge acquisition. Proceedings of the Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-18).

@inproceedings{Rostami2018MultiAgent,
  title = {Multi-agent distributed lifelong learning for collective knowledge acquisition},
  author = {Rostami, Mohammad and Kolouri, Soheil and Kim, Kyungnam and Eaton, Eric},
  booktitle = {Proceedings of the Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-18)},
  year = {2018},
  group = {journals},
  preprint = {Rostami2018MultiAgent.pdf},
  area = {LLMTL},
  funding = {AFOSR}
}

    Lifelong machine learning methods acquire knowledge over 
    a series of consecutive tasks, continually building upon 
    their experience.  Current lifelong learning algorithms 
    rely upon a single learning agent that has centralized 
    access to all data. In this paper, we extend the idea of 
    lifelong learning from a single agent to a network of 
    multiple agents that collectively learn a series of 
    tasks. Each agent faces some (potentially unique) set of 
    tasks; the key idea is that knowledge learned from these 
    tasks may benefit other agents trying to learn different 
    (but related) tasks.  Our Collective Lifelong Learning 
    Algorithm (CoLLA) provides an efficient way for a network 
    of agents to share their learned knowledge in a 
    distributed and decentralized manner, while eliminating 
    the need to share locally observed data.  We provide 
    theoretical guarantees for robust performance of the 
    algorithm and empirically demonstrate that CoLLA 
    outperforms existing approaches for distributed 
    multi-task learning on a variety of datasets.

Mocanu, D. C., Ammar, H. B., Puig, L., Eaton, E., & Liotta, A. (2017). Estimating 3D trajectories from 2D projections via disjunctive factored four-way conditional restricted Boltzmann machines. Pattern Recognition, 69, 325–335.

@inproceedings{Mocanu2017Estimating,
  title = {Estimating 3D trajectories from 2D projections via disjunctive factored four-way conditional restricted Boltzmann machines},
  author = {Mocanu, Decebal Constantin and Ammar, Haitham Bou and Puig, Luis and Eaton, Eric and Liotta, Antonio},
  journal = {Pattern Recognition},
  volume = {69},
  pages = {325--335},
  month = sep,
  year = {2017},
  group = {journals},
  link = {https://www.sciencedirect.com/science/article/abs/pii/S003132031730167X},
  area = {LLMTL}
}

     Estimation, recognition, and near-future prediction of 3D trajectories based on their 
     two dimensional projections available from one camera source is an exceptionally 
     difficult problem due to uncertainty in the trajectories and environment, high 
     dimensionality of the specific trajectory states, lack of enough labeled data and so 
     on. In this article, we propose a solution to solve this problem based on a novel 
     deep learning model dubbed disjunctive factored four-way conditional restricted 
     Boltzmann machine (DFFW-CRBM). Our method improves state-of-the-art deep learning 
     techniques for high dimensional time-series modeling by introducing a novel tensor 
     factorization capable of driving forth order Boltzmann machines to considerably lower 
     energy levels, at no computational costs. DFFW-CRBMs are capable of accurately 
     estimating, recognizing, and performing near-future prediction of three-dimensional 
     trajectories from their 2D projections while requiring limited amount of labeled 
     data. We evaluate our method on both simulated and real-world data, showing its 
     effectiveness in predicting and classifying complex ball trajectories and human 
     activities.

Clingerman, C., & Eaton, E. (2017). Lifelong machine learning with Gaussian processes. Proceedings of the European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD-17).

@inproceedings{Clingerman2017Lifelong,
  title = {Lifelong machine learning with Gaussian processes},
  author = {Clingerman, Christopher and Eaton, Eric},
  booktitle = {Proceedings of the European Conference on Machine Learning \& Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD-17)},
  year = {2017},
  group = {journals},
  preprint = {Clingerman2017Lifelong.pdf},
  area = {LLMTL}
}

     Recent developments in lifelong machine learning have demonstrated
     that it is possible to learn multiple tasks consecutively, transferring
     knowledge between those tasks to accelerate learning and improve performance.
     However, these methods are limited to using linear parametric
     base learners, substantially restricting the predictive power of the resulting
     models. We present a lifelong learning algorithm that can support nonparametric
     models, focusing on Gaussian processes. To enable efficient
     online transfer between Gaussian process models, our approach assumes
     a factorized formulation of the covariance functions, and incrementally
     learns a shared sparse basis for the models’ parameterizations. We show
     that this lifelong learning approach is highly computationally efficient,
     and outperforms existing methods on a variety of data sets.

Eaton, E. (2017). Teaching integrated AI through interdisciplinary project-driven courses. AI Magazine, 38(2), 13–21.

@inproceedings{Eaton2017Teaching,
  title = {Teaching integrated {AI} through interdisciplinary project-driven courses},
  author = {Eaton, Eric},
  journal = {AI Magazine},
  volume = {38},
  number = {2},
  pages = {13--21},
  year = {2017},
  group = {journals},
  link = {https://doi.org/10.1609/aimag.v38i2.2730},
  link_pdf = {https://www.aaai.org/ojs/index.php/aimagazine/article/view/2730/2631},
  area = {Education}
}

     Different subfields of AI (such as vision, learning, reasoning, planning, and others) 
     are often studied in isolation, both in individual courses and in the research 
     literature. This promulgates the idea that these different AI capabilities can easily 
     be integrated later, whereas, in practice, developing integrated AI systems remains 
     an open challenge for both research and industry. Interdisciplinary project-driven 
     courses can fill this gap in AI education, providing challenging problems that 
     require the integration of multiple AI methods. This article explores teaching 
     integrated AI through two project-driven courses: a capstone-style graduate course in 
     advanced robotics, and an undergraduate course on computational sustainability and 
     assistive computing. In addition to studying the integration of AI techniques, these 
     courses provide students with practical applications experience and exposure to 
     social issues of AI and computing. My hope is that other instructors find these 
     courses as useful examples for constructing their own project-driven courses to teach 
     integrated AI.

Eaton, E., Mucchiani, C., Mohan, M., Isele, D., Luna, J. M., & Clingerman, C. (2016, July). Design of a low-cost platform for autonomous mobile service robots. IJCAI-16 Workshop on Autonomous Mobile Service Robots.

@inproceedings{Eaton2016Design,
  author = {Eaton, Eric and Mucchiani, Caio and Mohan, Mayumi and Isele, David and Luna, Jose Marcio and Clingerman, Christopher},
  year = {2016},
  title = {Design of a low-cost platform for autonomous mobile service robots},
  booktitle = {IJCAI-16 Workshop on Autonomous Mobile Service Robots},
  month = jul,
  group = {refereedworkshop},
  preprint = {Eaton2016Design.pdf},
  slides = {Eaton2016Design-slides.pdf},
  poster = {Eaton2016Design-poster.pdf},
  area = {Other},
  funding = {ONR, AFOSR}
}

    Most current autonomous mobile service robots are 
    either expensive commercial platforms or custom 
    manufactured for research environments, limiting 
    their availability. We present the design for a low-cost 
    service robot based on the widely used TurtleBot 2 
    platform, with the goal of making service 
    robots affordable and accessible to the research, educational, 
    and hobbyist communities. 
    Our design uses a set of simple and inexpensive 
    modifications to transform the TurtleBot 2 into a 
    4.5ft (1.37m) tall tour-guide or telepresence-style 
    robot, capable of performing a wide variety of indoor 
    service tasks. The resulting platform provides 
    a shoulder-height touchscreen and 3D camera for 
    interaction, an optional low-cost arm for manipulation, 
    enhanced onboard computation, autonomous 
    charging, and up to 6 hours of runtime. The resulting 
    platform can support many of the tasks 
    performed by significantly more expensive service 
    robots. For compatibility with existing software 
    packages, the service robot runs the Robot Operating 
    System (ROS).

Isele, D., Luna, J. M., Eaton, E., de la Cruz, G. V., Irwin, J., Kallaher, B., & Taylor, M. E. (2016, October). Lifelong Learning for Disturbance Rejection on Mobile Robots. Proceedings of the International Conference on Intelligent Robots and Systems (IROS-16).

@inproceedings{Isele2016Lifelong,
  author = {Isele, David and Luna, Jose Marcio and Eaton, Eric and {de la Cruz}, Gabriel V. and Irwin, James and Kallaher, Brandon and Taylor, Matthew E.},
  year = {2016},
  title = {Lifelong Learning for Disturbance Rejection on Mobile Robots},
  booktitle = {Proceedings of the International Conference on Intelligent Robots and Systems (IROS-16)},
  month = oct,
  publisher = {IEEE/RSJ},
  group = {journals},
  preprint = {Isele2016Lifelong.pdf},
  link_video = {https://youtu.be/u7pkhLx0FQ0},
  area = {LLMTL},
  funding = {ONR, AFOSR}
}

    No two robots are exactly the same—even for a given 
    model of robot, different units will require slightly 
    different controllers. Furthermore, because robots 
    change and degrade over time, a controller will need 
    to change over time to remain optimal. This paper 
    leverages lifelong learning in order to learn 
    controllers for different robots. In particular, 
    we show that by learning a set of control policies 
    over robots with different (unknown) motion models, 
    we can quickly adapt to changes in the robot, or 
    learn a controller for a new robot with a unique 
    set of disturbances. Furthermore, the approach is 
    completely model-free, allowing us to apply this 
    method to robots that have not, or cannot, be 
    fully modeled.

Valdes, G., Luna, J. M., Eaton, E., Simone II, C. B., Ungar, L. H., & Solberg, T. D. (2016). MediBoost: a Patient Stratification Tool for Interpretable Decision Making in the Era of Precision Medicine. Scientific Reports, 6, 37854.

@inproceedings{Valdes2016MediBoost,
  title = {MediBoost: a Patient Stratification Tool for Interpretable Decision Making in the Era of Precision Medicine},
  author = {Valdes, Gilmer and Luna, Jose Marcio and Eaton, Eric and {Simone II}, Charles B. and Ungar, Lyle H. and Solberg, Timothy D.},
  journal = {Scientific Reports},
  volume = {6},
  pages = {37854},
  year = {2016},
  month = nov,
  group = {journals},
  link = {http://www.nature.com/articles/srep37854},
  link_pdf = {http://www.nature.com/articles/srep37854.pdf},
  link_supplement = {http://www.nature.com/article-assets/npg/srep/2016/161130/srep37854/extref/srep37854-s1.pdf},
  area = {Other}
}

   Machine learning algorithms that are both interpretable 
   and accurate are essential in applications such as 
   medicine where errors can have a dire consequence. 
   Unfortunately, there is currently a tradeoff between 
   accuracy and interpretability among state-of-the-art 
   methods. Decision trees are interpretable and are 
   therefore used extensively throughout medicine for 
   stratifying patients. Current decision tree algorithms, 
   however, are consistently outperformed in accuracy by 
   other, less-interpretable machine learning models, such 
   as ensemble methods. We present MediBoost, a novel 
   framework for constructing decision trees that retain 
   interpretability while having accuracy similar to 
   ensemble methods, and compare MediBoost’s performance 
   to that of conventional decision trees and ensemble 
   methods on 13 medical classification problems. MediBoost 
   significantly outperformed current decision tree 
   algorithms in 11 out of 13 problems, giving accuracy 
   comparable to ensemble methods. The resulting trees are 
   of the same type as decision trees used throughout 
   clinical practice but have the advantage of improved 
   accuracy. Our algorithm thus gives the best of both 
   worlds: it grows a single, highly interpretable tree 
   that has the high accuracy of ensemble methods.

Isele, D., Rostami, M., & Eaton, E. (2016, July). Using task features for zero-shot knowledge transfer in lifelong learning. Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-16).

@inproceedings{Isele2016Using,
  author = {Isele, David and Rostami, Mohammad and Eaton, Eric},
  year = {2016},
  title = {Using task features for zero-shot knowledge transfer in lifelong learning},
  booktitle = {Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-16)},
  month = jul,
  note = {Awarded sole IJCAI-16 Distinguished Student Paper},
  group = {journals},
  preprint = {Isele2016Using.pdf},
  slides = {Isele2016Using-slides.pdf},
  poster = {Isele2016Using-poster.pdf},
  area = {LLMTL},
  funding = {ONR, AFOSR}
}

   Knowledge transfer between tasks can improve the performance of learned 
   models, but requires an accurate estimate of the inter-task relationships 
   to identify the relevant knowledge to transfer. These inter-task 
   relationships are typically estimated based on training data for each 
   task, which is inefficient in lifelong learning settings where the goal 
   is to learn each consecutive task rapidly from as little data as possible. 
   To reduce this burden, we develop a lifelong reinforcement learning method 
   based on coupled dictionary learning that incorporates high-level task 
   descriptors to model the inter-task relationships. We show that using 
   task descriptors improves the performance of the learned task policies, 
   providing both theoretical justification for the benefit and empirical 
   demonstration of the improvement across a variety of dynamical control 
   problems. Given only the descriptor for a new task, the lifelong learner 
   is also able to accurately predict the task policy through zero-shot 
   learning using the coupled dictionary, eliminating the need to pause to 
   gather training data before addressing the task.

Isele, D., Luna, J. M., Eaton, E., de la Cruz, G. V., Irwin, J., Kallaher, B., & Taylor, M. E. (2016, May). Work in Progress: Lifelong Learning for Disturbance Rejection on Mobile Robots. Proceedings of the AAMAS’16 Workshop on Adaptive Learning Agents.

@inproceedings{Isele2016Work,
  author = {Isele, David and Luna, Jose Marcio and Eaton, Eric and {de la Cruz}, Gabriel V. and Irwin, James and Kallaher, Brandon and Taylor, Matthew E.},
  year = {2016},
  title = {Work in Progress: Lifelong Learning for Disturbance Rejection on Mobile Robots},
  booktitle = {Proceedings of the AAMAS'16 Workshop on Adaptive Learning Agents},
  month = may,
  note = {Superseded by the IROS-16 paper: Lifelong Learning for Disturbance Rejection on Mobile Robots.},
  group = {refereedworkshop},
  preprint = {Isele2016Work.pdf},
  slides = {Isele2016Work-slides.pdf},
  area = {LLMTL},
  funding = {ONR, AFOSR}
}

    No two robots are exactly the same – even for a given model of robot, 
    different units will require slightly different controllers. Furthermore, 
    because robots change and degrade over time, a controller will need to 
    change over time to remain optimal. This paper leverages lifelong 
    learning in order to learn controllers for different robots. In particular, 
    we show that by learning a set of control policies over robots with 
    different (unknown) motion models, we can quickly adapt to changes in the 
    robot, or learn a controller for a new robot with a unique set of 
    disturbances. Further, the approach is completely model-free, allowing us 
    to apply this method to robots that have not, or cannot, be fully modeled. 
    These preliminary results are an initial step towards learning robust 
    fault-tolerant control for arbitrary robots.

Ammar, H. B., Eaton, E., Luna, J. M., & Ruvolo, P. (2015, July). Autonomous cross-domain knowledge transfer in lifelong policy gradient reinforcement learning. Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-15).

@inproceedings{BouAmmar2015Autonomous,
  author = {Ammar, Haitham Bou and Eaton, Eric and Luna, Jose Marcio and Ruvolo, Paul},
  year = {2015},
  title = {Autonomous cross-domain knowledge transfer in lifelong policy gradient reinforcement learning},
  booktitle = {Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-15)},
  month = jul,
  note = {Finalist for IJCAI-15 Distinguished Paper award},
  group = {journals},
  preprint = {BouAmmar2015Autonomous.pdf},
  area = {LLMTL},
  funding = {ONR, AFOSR}
}

   Online multi-task learning is an important capability for lifelong 
   learning agents, enabling them to acquire models for diverse tasks 
   over time and rapidly learn new tasks by building upon prior experience. 
   However, recent progress toward lifelong reinforcement learning (RL) 
   has been limited to learning from within a single task domain.  For 
   truly versatile lifelong learning, the agent must be able to autonomously 
   transfer knowledge between different task domains. A few methods for 
   cross-domain transfer have been developed, but these methods are 
   computationally inefficient for scenarios where the agent must learn 
   tasks consecutively.

   In this paper, we develop the first cross-domain lifelong RL framework. 
   Our approach efficiently optimizes a shared repository of transferable 
   knowledge and learns projection matrices that specialize that knowledge 
   to different task domains. We provide rigorous theoretical guarantees on 
   the stability of this approach, and empirically evaluate its performance 
   on diverse dynamical systems.  Our results show that the proposed method 
   can learn effectively from interleaved task domains and rapidly acquire 
   high performance in new domains.

Ammar, H. B., Tutunov, R., & Eaton, E. (2015, July). Safe policy search for lifelong reinforcement learning with sublinear regret. Proceedings of the 32nd International Conference on Machine Learning (ICML-15).

@inproceedings{BouAmmar2015Safe,
  author = {Ammar, Haitham Bou and Tutunov, Rasul and Eaton, Eric},
  year = {2015},
  title = {Safe policy search for lifelong reinforcement learning with sublinear regret},
  booktitle = {Proceedings of the 32nd International Conference on Machine Learning (ICML-15)},
  month = jul,
  group = {journals},
  preprint = {BouAmmar2015Safe.pdf},
  slides = {slides-BouAmmar2015Safe.pdf},
  poster = {poster-BouAmmar2015Safe.pdf},
  area = {LLMTL},
  funding = {ONR, AFOSR}
}

   Lifelong reinforcement learning provides a promising framework for 
   developing versatile agents that can accumulate knowledge over a 
   lifetime of experience and rapidly learn new tasks by building 
   upon prior knowledge.  However, current lifelong learning methods 
   exhibit non-vanishing regret as the amount of experience increases, 
   and include limitations that can lead to suboptimal or unsafe 
   control policies.  To address these issues, we develop a lifelong 
   policy gradient learner that operates in an adversarial setting to 
   learn multiple tasks online while enforcing safety constraints on 
   the learned policies.  We demonstrate, for the first time, 
   sublinear regret for lifelong policy search, and validate our 
   algorithm on several benchmark dynamical systems and an application 
   to quadrotor control.

Ammar, H. B., Eaton, E., Ruvolo, P., & Taylor, M. E. (2015, January). Unsupervised cross-domain transfer in policy gradient reinforcement learning via manifold alignment. Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI-15).

@inproceedings{BouAmmar2015Unsupervised,
  author = {Ammar, Haitham Bou and Eaton, Eric and Ruvolo, Paul and Taylor, Matthew E.},
  title = {Unsupervised cross-domain transfer in policy gradient reinforcement learning via manifold alignment},
  booktitle = {Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI-15)},
  month = jan,
  year = {2015},
  note = {Acceptance rate: 27%},
  group = {journals},
  preprint = {BouAmmar2015Unsupervised.pdf},
  slides = {slides-BouAmmar2015Unsupervised.pdf},
  poster = {poster-BouAmmar2015Unsupervised.pdf},
  area = {TransferLearning},
  funding = {ONR, AFOSR}
}

  The success of applying policy gradient reinforcement learning (RL) to 
  difficult control tasks hinges crucially on the ability to determine a 
  sensible initialization for the policy. Transfer learning methods tackle 
  this problem by reusing knowledge gleaned from solving other related tasks. 
  In the case of multiple task domains, these algorithms require an inter-task 
  mapping to facilitate knowledge transfer across domains. However, there are 
  currently no general methods to learn an inter-task mapping without requiring 
  either background knowledge that is not typically present in RL settings, or 
  an expensive analysis of an exponential number of inter-task mappings in the 
  size of the state and action spaces. This paper introduces an autonomous 
  framework that uses unsupervised manifold alignment to learn intertask mappings 
  and effectively transfer samples between different task domains. Empirical 
  results on diverse dynamical systems, including an application to quadrotor 
  control, demonstrate its effectiveness for cross-domain transfer in the context 
  of policy gradient RL.

2010 - 2014

Ammar, H. B., Eaton, E., Taylor, M. E., Mocanu, D., Driessens, K., Weiss, G., & Tuyls, K. (2014, July). An automated measure of MDP similarity for transfer in reinforcement learning. Proceedings of the AAAI’14 Workshop on Machine Learning for Interactive Systems.

@inproceedings{BouAmmar2014Automated,
  author = {Ammar, Haitham Bou and Eaton, Eric and Taylor, Matthew E. and Mocanu, Decebal and Driessens, Kurt and Weiss, Gerhard and Tuyls, Karl},
  year = {2014},
  title = {An automated measure of {MDP} similarity for transfer in reinforcement learning},
  booktitle = {Proceedings of the AAAI'14 Workshop on Machine Learning for Interactive Systems},
  month = jul,
  group = {refereedworkshop},
  preprint = {BouAmmar2014Automated.pdf},
  slides = {slides-BouAmmar2014Automated.pdf},
  poster = {poster-BouAmmar2014Automated.pdf},
  area = {TransferLearning},
  funding = {ONR, AFOSR}
}

   Transfer learning can improve the reinforcement learning
   of a new task by allowing the agent to reuse knowledge 
   acquired from other source tasks. Despite their success, 
   transfer learning methods rely on having relevant source 
   tasks; transfer from inappropriate tasks can inhibit 
   performance on the new task. For fully autonomous 
   transfer, it is critical to have a method for automatically 
   choosing relevant source tasks, which requires a similarity 
   measure between Markov Decision Processes (MDPs). This 
   issue has received little attention, and is therefore still 
   a largely open problem.
   
   This paper presents a data-driven automated similarity measure 
   for MDPs. This novel measure is a significant step toward 
   autonomous reinforcement learning transfer, allowing agents 
   to: (1) characterize when transfer will be useful and, 
   (2) automatically select tasks to use for transfer. The 
   proposed measure is based on the reconstruction error of a 
   restricted Boltzmann machine that attempts to model the 
   behavioral dynamics of the two MDPs being compared. Empirical 
   results illustrate that this measure is correlated with the 
   performance of transfer and therefore can be used to identify 
   similar source tasks for transfer learning.

Eaton, E., Gomes, C., & Williams, B. (2014). Computational Sustainability. AI Magazine, 35(2), 3–7.

@inproceedings{Eaton2014CompSustainability,
  author = {Eaton, Eric and Gomes, Carla and Williams, Brian},
  title = {Computational Sustainability},
  journal = {AI Magazine},
  volume = {35},
  number = {2},
  pages = {3--7},
  year = {2014},
  month = jun,
  group = {journals},
  preprint = {Eaton2014CompSustainability.pdf},
  area = {CompSus}
}

     Computational sustainability problems, which exist in 
     dynamic environments with high amounts of uncertainty, 
     provide a variety of unique challenges to artificial 
     intelligence research and the opportunity for 
     significant impact upon our collective future. This 
     editorial provides an overview of artificial 
     intelligence for computational sustainability, and 
     introduces this special issue of AI Magazine.

Eaton, E., desJardins, M., & Jacob, S. (2014). Multi-view constrained clustering with an incomplete mapping between views. Knowledge and Information Systems, 38(1), 231–257.

@inproceedings{Eaton2012MultiView,
  author = {Eaton, Eric and desJardins, Marie and Jacob, Sara},
  title = {Multi-view constrained clustering with an incomplete mapping between views},
  journal = {Knowledge and Information Systems},
  volume = {38},
  number = {1},
  pages = {231--257},
  month = jan,
  year = {2014},
  group = {journals},
  preprint = {Eaton2012MultiView.pdf},
  link = {http://link.springer.com/content/pdf/10.1007%2Fs10115-012-0577-7},
  area = {ConstClustering},
  funding = {ONR, Lockheed Martin}
}

 	Multi-view learning algorithms typically assume a 
 	complete bipartite mapping between the different 
 	views in order to exchange information during the 
 	learning process.   However, many applications 
 	provide only a partial mapping between the views, 
 	creating a challenge for current methods.  To 
 	address this problem, we propose a multi-view 
 	algorithm based on constrained clustering that can 
 	operate with an incomplete mapping.  Given a set 
 	of pairwise constraints in each view, our approach 
 	propagates these constraints using a local 
 	similarity measure to those instances that can be 
 	mapped to the other views, allowing the propagated 
 	constraints to be transferred across views via the 
 	partial mapping.  It uses co-EM to iteratively 
 	estimate the propagation within each view based on 
 	the current clustering model, transfer the 
 	constraints across views, and then update the 
 	clustering model.  By alternating the learning 
 	process between views, this approach produces a 
 	unified clustering model that is consistent with 
 	all views.  We show that this approach 
 	significantly improves clustering performance over 
 	several other methods for transferring constraints 
 	and allows multi-view clustering to be reliably 
 	applied when given a limited mapping between the 
 	views.  Our evaluation reveals that the propagated 
 	constraints have high precision with respect to 
 	the true clusters in the data, explaining their 
 	benefit to clustering performance in both single- 
 	and multi-view learning scenarios.

Sreenivasan, V. P., Ammar, H. B., & Eaton, E. (2014, July). Online Multi-Task Gradient Temporal-Difference Learning. Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI-14).

@inproceedings{VishnuPS2014Online,
  author = {Sreenivasan, Vishnu Purushothaman and Ammar, Haitham Bou and Eaton, Eric},
  title = {Online Multi-Task Gradient Temporal-Difference Learning},
  note = {[Student Abstract]},
  booktitle = {Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI-14)},
  year = {2014},
  month = jul,
  group = {refereedshort},
  preprint = {VishnuPS2014Online.pdf},
  area = {LLMTL},
  funding = {ONR, AFOSR}
}

 	We develop an online multi-task formulation of 
 	model-based gradient temporal-difference (GTD) 
 	reinforcement learning.  Our approach enables 
 	an autonomous RL agent to accumulate knowledge 
 	over its lifetime and efficiently share this 
 	knowledge between tasks to accelerate learning. 
 	Rather than learning a policy for an RL task 
 	tabula rasa, as in standard GTD, our approach 
 	rapidly learns a high performance policy by 
 	building upon the agent’s previously learned 
 	knowledge. Our preliminary results on controlling 
 	different mountain car tasks demonstrates that 
 	GTD-ELLA significantly improves learning over 
 	standard GTD(0).

Ammar, H. B., Eaton, E., Ruvolo, P., & Taylor, M. E. (2014, June). Online Multi-Task Learning for Policy Gradient Methods. Proceedings of the 31st International Conference on Machine Learning (ICML-14).

@inproceedings{BouAmmar2014Online,
  author = {Ammar, Haitham Bou and Eaton, Eric and Ruvolo, Paul and Taylor, Matthew E.},
  title = {Online Multi-Task Learning for Policy Gradient Methods},
  booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
  year = {2014},
  month = jun,
  group = {journals},
  preprint = {BouAmmar2014Online.pdf},
  area = {LLMTL},
  funding = {ONR, AFOSR}
}

    Policy gradient algorithms have shown considerable recent 
    success in solving high-dimensional sequential 
    decision-making (SDM) tasks, particularly in robotics.  
    However, these methods often require extensive experience 
    in a domain to achieve high performance. To make agents 
    more sample-efficient, we developed a multi-task policy 
    gradient method to learn SDM tasks consecutively, 
    transferring knowledge between tasks to accelerate 
    learning.  Our approach provides robust theoretical 
    guarantees and we show empirically that it dramatically 
    accelerates learning on a variety of dynamical systems, 
    including an application to quadcopter control.

Ruvolo, P., & Eaton, E. (2014, July). Online Multi-Task Learning via Sparse Dictionary Optimization. Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI-14).

@inproceedings{Ruvolo2014Online,
  author = {Ruvolo, Paul and Eaton, Eric},
  title = {Online Multi-Task Learning via Sparse Dictionary Optimization},
  booktitle = {Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI-14)},
  year = {2014},
  month = jul,
  group = {journals},
  preprint = {Ruvolo2014Online.pdf},
  slides = {slides-Ruvolo2014Online.pdf},
  poster = {poster-Ruvolo2014Online.pdf},
  area = {LLMTL},
  funding = {ONR, AFOSR}
}

 	This paper develops an efficient online algorithm 
 	for learning multiple consecutive tasks based on 
 	the K-SVD algorithm for sparse dictionary optimization.  
 	We first derive a batch multi-task learning method that 
 	builds upon K-SVD, and then extend the batch algorithm 
 	to train models online in a lifelong learning setting.  
 	The resulting method has lower computational 
 	complexity than other current lifelong learning 
 	algorithms while maintaining nearly identical model 
 	performance.  Additionally, the proposed method offers 
 	an alternate formulation for lifelong learning that 
 	supports both task and feature similarity matrices.

Ruvolo, P., & Eaton, E. (2013, July). Active Task Selection for Lifelong Machine Learning. Proceedings of the 27th AAAI Conference on Artificial Intelligence (AAAI-13).

@inproceedings{Ruvolo2013Active,
  author = {Ruvolo, Paul and Eaton, Eric},
  title = {Active Task Selection for Lifelong Machine Learning},
  booktitle = {Proceedings of the 27th AAAI Conference on Artificial Intelligence (AAAI-13)},
  year = {2013},
  month = jul,
  group = {journals},
  preprint = {Ruvolo2013Active.pdf},
  link = {},
  slides = {slides-Ruvolo2013Active.pdf},
  poster = {poster-Ruvolo2013Active.pdf},
  area = {LLMTL},
  funding = {ONR}
}

 	In a lifelong learning framework, an agent acquires 
 	knowledge incrementally over consecutive learning 
 	tasks, continually building upon its experience.  
 	Recent lifelong learning algorithms have achieved 
 	nearly identical performance to batch multi-task 
 	learning methods while reducing learning time by 
 	three orders of magnitude.  In this paper, we 
 	further improve the scalability of lifelong learning 
 	by developing curriculum selection methods that 
 	enable an agent to actively select the next task to 
 	learn in order to maximize performance on future 
 	learning tasks.  We demonstrate that active task 
 	selection is highly reliable and effective, allowing 
 	an agent to learn high performance models using up 
 	to 50% fewer tasks than when the agent has no 
 	control over the task order.  We also explore a 
 	variant of transfer learning in the lifelong learning 
 	setting in which the agent can focus knowledge 
 	acquisition toward a particular target task.

Ruvolo, P., & Eaton, E. (2013, June). ELLA: An Efficient Lifelong Learning Algorithm. Proceedings of the 30th International Conference on Machine Learning (ICML-13).

@inproceedings{Ruvolo2013ELLA,
  author = {Ruvolo, Paul and Eaton, Eric},
  title = {ELLA: An Efficient Lifelong Learning Algorithm},
  booktitle = {Proceedings of the 30th International Conference on Machine Learning (ICML-13)},
  year = {2013},
  month = jun,
  group = {journals},
  preprint = {Ruvolo2013ELLA.pdf},
  slides = {slides-Ruvolo2013ELLA.pdf},
  poster = {poster-Ruvolo2013ELLA.pdf},
  area = {LLMTL},
  funding = {ONR}
}

 	The problem of learning multiple consecutive tasks, 
 	known as lifelong learning, is of great 
 	importance to the creation of intelligent, 
 	general-purpose, and flexible machines.  In this 
 	paper, we develop a method for online multi-task 
 	learning in the lifelong learning setting.  The 
 	proposed Efficient Lifelong Learning Algorithm 
 	(ELLA) maintains a sparsely shared basis for all 
 	task models, transfers knowledge from the basis 
 	to learn each new task, and refines the basis 
 	over time to maximize performance across all 
 	tasks. We show that ELLA has strong connections 
 	to both online dictionary learning for sparse 
 	coding and state-of-the-art batch multi-task 
 	learning methods, and provide robust theoretical 
 	performance guarantees.  We show empirically that 
 	ELLA yields nearly identical performance to batch 
 	multi-task learning while learning tasks 
 	sequentially in three orders of magnitude (over 
 	1,000x) less time.

Ruvolo, P., & Eaton, E. (2013, June). Online Multi-Task Learning based on K-SVD. Proceedings of the ICML 2013 Workshop on Theoretically Grounded Transfer Learning.

@inproceedings{Ruvolo2013Online,
  author = {Ruvolo, Paul and Eaton, Eric},
  title = {Online Multi-Task Learning based on K-SVD},
  booktitle = {Proceedings of the ICML 2013 Workshop on Theoretically Grounded Transfer Learning},
  year = {2013},
  month = jun,
  note = {Superseded by the AAAI-14 paper: Online Multi-Task Learning via Sparse Dictionary Optimization.},
  group = {refereedworkshop},
  preprint = {Ruvolo2013Online.pdf},
  area = {LLMTL},
  funding = {ONR}
}

 	This paper develops an efficient online algorithm 
 	based on K-SVD for learning multiple consecutive 
 	tasks.  We first derive a batch multi-task learning 
 	method that builds upon the K-SVD algorithm, and 
 	then extend the batch algorithm to train models 
 	online in a lifelong learning setting.  The 
 	resulting method has lower computational complexity 
 	than other current lifelong learning algorithms 
 	while maintaining nearly identical performance.  
 	Additionally, the proposed method offers an 
 	alternate formulation for lifelong learning that 
 	supports both task and feature similarity matrices.

Ruvolo, P., & Eaton, E. (2013, March). Scalable Lifelong Learning with Active Task Selection. Proceedings of the AAAI 2013 Spring Symposium on Lifelong Machine Learning.

@inproceedings{Ruvolo2013Scalable,
  author = {Ruvolo, Paul and Eaton, Eric},
  title = {Scalable Lifelong Learning with Active Task Selection},
  booktitle = {Proceedings of the AAAI 2013 Spring Symposium on Lifelong Machine Learning},
  year = {2013},
  location = {Stanford, CA},
  month = mar,
  note = {Superseded by the AAAI-13 paper: Active Task Selection for Lifelong Machine Learning.},
  group = {refereedworkshop},
  preprint = {Ruvolo2013Scalable.pdf},
  area = {LLMTL},
  funding = {ONR}
}

    The recently developed Efficient Lifelong Learning 
    Algorithm (ELLA) acquires knowledge incrementally 
    over a sequence of tasks, learning a repository of 
    latent model components that are sparsely shared 
    between models.  ELLA shows strong performance in 
    comparison to other multi-task learning algorithms, 
    achieving nearly identical performance to batch 
    multi-task learning methods while learning tasks 
    sequentially in three orders of magnitude (over 
    1,000x) less time.  In this paper, we evaluate 
    several curriculum selection methods that allow 
    ELLA to actively select the next task for learning 
    in order to maximize performance on future learning 
    tasks.  Through experiments with three real and one 
    synthetic data set, we demonstrate that active 
    curriculum selection allows an agent to learn up to 
    50% more efficiently than when the agent has no 
    control over the task order.

Eaton, E., & Mansbach, R. (2012). A spin-glass model for semi-supervised community detection. Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI-12), 900–906.

@inproceedings{Eaton2012SpinGlass,
  author = {Eaton, Eric and Mansbach, Rachael},
  title = {A spin-glass model for semi-supervised community detection},
  booktitle = {Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI-12)},
  month = jul,
  location = {Toronto, Canada},
  publisher = {AAAI Press},
  pages = {900--906},
  year = {2012},
  group = {journals},
  preprint = {Eaton2012SpinGlass.pdf},
  poster = {poster-Eaton2012SpinGlass.pdf},
  area = {RelationalNetwork},
  funding = {ONR}
}

 	Current modularity-based community detection methods 
 	show decreased performance as relational networks 
 	become increasingly noisy.  These methods also yield 
 	a large number of diverse community structures as 
 	solutions, which is problematic for applications 
 	that impose constraints on the acceptable solutions 
 	or in cases where the user is focused on specific 
 	communities of interest.  To address both of these 
 	problems, we develop a semi-supervised spin-glass 
 	model that enables current community detection 
 	methods to incorporate background knowledge in the 
 	forms of individual labels and pairwise constraints.  
 	Unlike current methods, our approach shows robust 
 	performance in the presence of noise in the 
 	relational network, and the ability to guide the 
 	discovery process toward specific community 
 	structures.  We evaluate our algorithm on several 
 	benchmark networks and a new political sentiment 
 	network representing cooperative events between 
 	nations that was mined from news articles over 
 	six years.

Fisher, D., Dilkina, B., Eaton, E., & Gomes, C. (2012, July). Incorporating computational sustainability into AI education through a freely-available, collectively-composed supplementary lab text. Proceedings of the 3rd AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-12).

@inproceedings{Fisher2012IncorporatingEAAI,
  author = {Fisher, Douglas and Dilkina, Bistra and Eaton, Eric and Gomes, Carla},
  title = {Incorporating computational sustainability into AI education through a freely-available, collectively-composed supplementary lab text},
  booktitle = {Proceedings of the 3rd AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-12)},
  month = jul,
  location = {Toronto, Canada},
  publisher = {AAAI Press},
  year = {2012},
  group = {refereedworkshop},
  preprint = {Fisher2012IncorporatingEAAI.pdf},
  poster = {poster-Fisher2012IncorporatingEAAI.pdf},
  area = {Education}
}

 	We introduce a laboratory text on environmental 
 	and societal sustainability applications that can 
 	be a supplemental resource for any undergraduate 
 	AI course. The lab text, entitled Artificial 
 	Intelligence for Computational Sustainability: 
 	A Lab Companion, is brand new and incomplete; 
 	freely available through Wikibooks; and open to 
 	community additions of projects, assignments, 
 	and explanatory material on AI for sustainability. 
 	The project adds to existing educational efforts 
 	of the computational sustainability community, 
 	encouraging the flow of knowledge from research to 
 	education and public outreach. Besides summarizing 
 	the laboratory book, this paper touches on its 
 	implications for integration of research and 
 	education, for communicating science to the public, 
 	and other broader impacts.

Fisher, D., Dilkina, B., Eaton, E., & Gomes, C. (2012, July). Incorporating computational sustainability into AI education through a freely-available, collectively-composed supplementary lab text [Oral Presentation]. Proceedings of the 3rd International Conference on Computational Sustainability (CompSust’12).
```
@inproceedings{Fisher2012IncorporatingCompSust,
  author = {Fisher, Douglas and Dilkina, Bistra and Eaton, Eric and Gomes, Carla},
  title = {Incorporating computational sustainability into AI education through a freely-available, collectively-composed supplementary lab text [Oral Presentation]},
  booktitle = {Proceedings of the 3rd International Conference on Computational Sustainability (CompSust'12)},
  month = jul,
  location = {Copenhagen, Denmark},
  year = {2012},
  group = {refereedworkshop},
  preprint = {Fisher2012IncorporatingCompSust.pdf},
  area = {Education},
  funding = {}
}
```
Oyen, D., Eaton, E., & Lane, T. (2012, April). Inferring tasks for improved network structure discovery. Working Notes of the Snowbird Learning Workshop.
```
@inproceedings{Oyen2012Inferring,
  author = {Oyen, Diane and Eaton, Eric and Lane, Terran},
  title = {Inferring tasks for improved network structure discovery},
  booktitle = {Working Notes of the Snowbird Learning Workshop},
  month = apr,
  location = {Snowbird, Utah},
  year = {2012},
  group = {refereedworkshop},
  preprint = {Oyen2012Inferring.pdf},
  area = {TransferLearning},
  funding = {ONR}
}
```

Eaton, E., & desJardins, M. (2011). Selective Transfer Between Learning Tasks Using Task-Based Boosting. Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI-11), 337–342.

@inproceedings{Eaton2011Selective,
  author = {Eaton, Eric and desJardins, Marie},
  title = {Selective Transfer Between Learning Tasks Using Task-Based Boosting},
  booktitle = {Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI-11)},
  month = aug,
  location = {San Francisco, CA},
  publisher = {AAAI Press},
  pages = {337--342},
  year = {2011},
  group = {journals},
  preprint = {Eaton2011Selective.pdf},
  supplement = {Eaton2011Selective-Supplement.pdf},
  area = {TransferLearning},
  funding = {ONR, NSF}
}

	The success of transfer learning on a target task 
	is highly dependent on the selected source data. 
	Instance transfer methods reuse data from the 
	source tasks to augment the training data for the 
	target task. If poorly chosen, this source data 
	may inhibit learning, resulting in negative 
	transfer. The current most widely used algorithm 
	for instance transfer, TrAdaBoost, performs poorly 
	when given irrelevant source data.

	We present a novel task-based boosting technique 
	for instance transfer that selectively chooses the 
	source knowledge to transfer to the target task.  
	Our approach performs boosting at both the instance 
	level and the task level, assigning higher weight to 
	those source tasks that show positive transferability 
	to the target task, and adjusting the weights of 
	individual instances within each source task via 
	AdaBoost.  We show that this combination of task- and 
	instance-level boosting significantly improves 
	transfer performance over existing instance transfer 
	algorithms when given a mix of relevant and irrelevant 
	source data, especially for small amounts of data on 
	the target task.

Eaton, E., & Lane, T. (2011, August). The Importance of Selective Knowledge Transfer for Lifelong Learning. AAAI-11 Workshop on Lifelong Learning from Sensorimotor Data.

@inproceedings{Eaton2011Importance,
  author = {Eaton, Eric and Lane, Terran},
  title = {The Importance of Selective Knowledge Transfer for Lifelong Learning},
  booktitle = {AAAI-11 Workshop on Lifelong Learning from Sensorimotor Data},
  month = aug,
  location = {San Francisco, CA},
  publisher = {AAAI Press},
  year = {2011},
  group = {refereedworkshop},
  preprint = {Eaton2011Importance.pdf},
  area = {TransferLearning},
  funding = {ONR}
}

	As knowledge transfer research progresses from single 
	transfer to lifelong learning scenarios, it becomes 
	increasingly important to properly select the source 
	knowledge that would best transfer to the target task.  
	In this position paper, we describe our previous work 
	on selective knowledge transfer and relate it to 
	problems in lifelong learning.  We also briefly discuss 
	our ongoing work to develop lifelong learning methods 
	capable of continual transfer between tasks and the 
	incorporation of guidance from an expert human user.

Eaton, E., Holness, G., & McFarlane, D. (2010). Interactive Learning using Manifold Geometry. Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI-10), 437–443.

@inproceedings{Eaton2010Interactive,
  author = {Eaton, Eric and Holness, Gary and McFarlane, Daniel},
  title = {Interactive Learning using Manifold Geometry},
  booktitle = {Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI-10)},
  month = jul,
  location = {Atlanta, GA},
  publisher = {AAAI Press},
  pages = {437--443},
  year = {2010},
  group = {journals},
  preprint = {Eaton2010Interactive.pdf},
  slides = {Eaton2010Interactive-Slides.ppt},
  area = {InteractiveLearning},
  funding = {NSF, Lockheed Martin}
}

	We present an interactive learning method that enables 
	a user to iteratively refine a regression model.  The 
	user examines the output of the model, visualized as 
	the vertical axis of a 2D scatterplot, and provides 
	corrections by repositioning individual data instances 
	to the correct output level. Each repositioned data 
	instance acts as a control point for altering the 
	learned model, using the geometry underlying the data.
	We capture the underlying structure of the data as a
	manifold, on which we compute a set of basis functions
	as the foundation for learning.  Our results show that
	manifold-based interactive learning improves performance 
	monotonically with each correction, outperforming 
	alternative approaches.

Wagstaff, K., desJardins, M., & Eaton, E. (2010). Modeling and learning user preferences over sets. Journal of Experimental & Theoretical Artificial Intelligence, 22(3), 237–268.

@inproceedings{Wagstaff2010Modeling,
  author = {Wagstaff, Kiri and desJardins, Marie and Eaton, Eric},
  year = {2010},
  title = {Modeling and learning user preferences over sets},
  journal = {Journal of Experimental & Theoretical Artificial Intelligence},
  volume = {22},
  number = {3},
  pages = {237--268},
  month = sep,
  group = {journals},
  preprint = {Wagstaff2010Modeling.pdf},
  area = {InteractiveLearning},
  funding = {NSF}
}

Although there has been significant research on modeling and learning user preferences for various types of objects, there has been relatively little work on the problem of representing and learning preferences over sets of objects.  We introduce a representation language, DD-PREF, that balances preferences for particular objects with preferences about the properties of the set.  Specifically, we focus on the depth of objects (i.e. preferences for specific attribute values over others) and on the diversity of sets (i.e. preferences for broad vs. narrow distributions of attribute values).  The DD-PREF framework is general and can incorporate additional object- and set-based preferences.  We describe a greedy algorithm, DD-Select, for selecting satisfying sets from a collection of new objects, given a preference in this language.  We show how preferences represented in DD-PREF can be learned from training data.  Experimental results are given for three domains: a blocks world domain with several different task-based preferences, a real-world music playlist collection, and rover image data gathered in desert training exercises.

Eaton, E., desJardins, M., & Jacob, S. (2010). Multi-View Clustering with Constraint Propagation for Learning with an Incomplete Mapping Between Views. Proceedings of the Conference on Information and Knowledge Management (CIKM’10), 389–398.

@inproceedings{Eaton2010MultiView,
  author = {Eaton, Eric and desJardins, Marie and Jacob, Sara},
  title = {Multi-View Clustering with Constraint Propagation for Learning with an Incomplete Mapping Between Views},
  booktitle = {Proceedings of the Conference on Information and Knowledge Management (CIKM'10)},
  month = oct,
  location = {Toronto, Ontario, Canada},
  publisher = {ACM Press},
  pages = {389--398},
  year = {2010},
  group = {journals},
  preprint = {Eaton2010MultiView.pdf},
  slides = {Eaton2010MultiView-Slides.pdf},
  area = {ConstClustering},
  funding = {NSF, Lockheed Martin}
}

  Multi-view learning algorithms typically assume a complete bipartite mapping between the different views in order to exchange information during the learning process.   However, many applications provide only a partial mapping between the views, creating a challenge for current methods.  To address this problem, we propose a multi-view algorithm based on constrained clustering that can operate with an incomplete mapping.  Given a set of pairwise constraints in each view, our approach propagates these constraints using a local similarity measure to those instances that can be mapped to the other views, allowing the propagated constraints to be transferred across views via the partial mapping.  It uses co-EM to iteratively estimate the propagation within each view based on the current clustering model, transfer the constraints across views, and update the clustering model, thereby learning a unified model for all views.  We show that this approach significantly improves clustering performance over several other methods for transferring constraints and allows multi-view clustering to be reliably applied when given a limited mapping between the views.

Journal Articles

Tutunov, R., Ammar, H. B., Jadbabaie, A., & Eaton, E. (2014). On the degree distribution of Pólya urn graph processes. ArXiv:1410.8515 Preprint.

@article{Tutunov2014DegreeDistribution,
  author = {Tutunov, Rasul and Ammar, Haitham Bou and Jadbabaie, Ali and Eaton, Eric},
  year = {2014},
  title = {On the degree distribution of P\'{o}lya urn graph processes},
  journal = {arXiv:1410.8515 Preprint},
  month = oct,
  group = {preprints},
  link_pdf = {http://arxiv.org/abs/1410.8515},
  area = {RelationalNetwork},
  funding = {ONR}
}

   This paper presents a tighter bound on the degree distribution of 
   arbitrary Polya urn graph processes, proving that the proportion of 
   vertices with degree d obeys a power-law distribution P(d) proportional 
   to d^(-gamma) for d <= n^(1/6 - epsilon) for any epsilon > 0, where n represents 
   the number of vertices in the network. Previous work by Bollobas et al. 
   formalized the well-known preferential attachment model of Barabasi and 
   Albert, and showed that the power-law distribution held for d <= n^(1/15) 
   with gamma = 3. Our revised bound represents a significant improvement 
   over existing models of degree distribution in scale-free networks, 
   where its tightness is restricted by the Azuma-Hoeffding concentration 
   inequality for martingales. We achieve this tighter bound through a 
   careful analysis of the first set of vertices in the network generation 
   process, and show that the newly acquired is at the edge of exhausting 
   Bollobas model in the sense that the degree expectation breaks down 
   for other powers.

Books

Eaton, E., Gomes, C., & Williams, B. (Eds.). (2014). Special Issue of AI Magazine on Computational Sustainability (Vol. 35, Numbers 2-3). AAAI Press. http://www.aaai.org/ojs/index.php/aimagazine/issue/view/206
```
@book{Eaton2014AIMagazine,
  editor = {Eaton, Eric and Gomes, Carla and Williams, Brian},
  title = {Special Issue of AI Magazine on Computational Sustainability},
  series = {AI Magazine},
  volume = {35},
  number = {2-3},
  month = jun,
  year = {2014},
  publisher = {AAAI Press},
  url = {http://www.aaai.org/ojs/index.php/aimagazine/issue/view/206},
  group = {editedvol},
  link_pdf = {http://www.aaai.org/ojs/index.php/aimagazine/issue/view/206},
  area = {CompSus}
}
```
(chair), E. E. (2013). Lifelong Machine Learning: Proceedings of the 2013 AAAI Spring Symposium. AAAI Press. http://www.aaai.org/Press/Reports/Symposia/Spring/ss-13-05.php
```
@book{Eaton2013AAAISSS,
  author = {(chair), Eric Eaton},
  title = {Lifelong Machine Learning: Proceedings of the 2013 AAAI Spring Symposium},
  series = {AAAI Technical Report SS-13-05},
  month = may,
  year = {2013},
  publisher = {AAAI Press},
  isbn = {ISBN 978-1-57735-602-8},
  url = {http://www.aaai.org/Press/Reports/Symposia/Spring/ss-13-05.php},
  group = {editedvol},
  link = {http://www.aaai.org/Press/Reports/Symposia/Spring/ss-13-05.php},
  area = {LLMTL}
}
```

2005 - 2009

Eaton, E., McFarlane, D., & Hofmann, M. (2009). Analysis of Complex Data Using Heterogeneous Relational Models (#DS-104-421-1610WP; Number #DS-104-421-1610WP, pp. 6 pgs). Lockheed Martin Advanced Technology Laboratories.
```
@techreport{Eaton2009Analysis,
  author = {Eaton, Eric and McFarlane, Dan and Hofmann, Martin},
  year = {2009},
  title = {Analysis of Complex Data Using Heterogeneous Relational Models},
  institution = {Lockheed Martin Advanced Technology Laboratories},
  number = {#DS-104-421-1610WP},
  pages = {6 pgs},
  month = mar,
  group = {techreport},
  funding = {Lockheed Martin}
}
```
Lomas, M., McFarlane, D., Eaton, E., Szczerba, R., & Franke, J. (2009). Dynamic Ensemble Planning for Tactical Hierarchies (#DS-104-421-1604WP; Number #DS-104-421-1604WP, pp. 4 pgs). Lockheed Martin Advanced Technology Laboratories.
```
@techreport{Lomas2009Dynamic,
  author = {Lomas, Meghann and McFarlane, Daniel and Eaton, Eric and Szczerba, Robert and Franke, Jerry},
  year = {2009},
  title = {Dynamic Ensemble Planning for Tactical Hierarchies},
  institution = {Lockheed Martin Advanced Technology Laboratories},
  number = {#DS-104-421-1604WP},
  pages = {4 pgs},
  month = mar,
  group = {techreport},
  funding = {Lockheed Martin}
}
```
Eaton, E., Guo, K., & Hofmann, M. (2009). Predicting and Verifying Effects of Cyber Operations from Indirect Observations (#DS-105-421-1598WP; Number #DS-105-421-1598WP, pp. 5 pgs). Lockheed Martin Advanced Technology Laboratories.
```
@techreport{Eaton2009Predicting,
  author = {Eaton, Eric and Guo, Katherine and Hofmann, Martin},
  year = {2009},
  title = {Predicting and Verifying Effects of Cyber Operations from Indirect Observations},
  institution = {Lockheed Martin Advanced Technology Laboratories},
  number = {#DS-105-421-1598WP},
  pages = {5 pgs},
  month = jan,
  group = {techreport},
  funding = {Lockheed Martin}
}
```
Eaton, E., Holness, G., & McFarlane, D. (2009). Situational Awareness through Interactive Learning (#DS-104-421-1607WP; Number #DS-104-421-1607WP, pp. 4 pgs). Lockheed Martin Advanced Technology Laboratories.
```
@techreport{Eaton2009Situational,
  author = {Eaton, Eric and Holness, Gary and McFarlane, Dan},
  year = {2009},
  title = {Situational Awareness through Interactive Learning},
  institution = {Lockheed Martin Advanced Technology Laboratories},
  number = {#DS-104-421-1607WP},
  pages = {4 pgs},
  month = mar,
  group = {techreport},
  funding = {Lockheed Martin}
}
```
Eaton, E., Guo, K., & Hofmann, M. (2008). Multimodal and Temporal Learning using Relational Networks (#DS-105-421-1583RFI; Number #DS-105-421-1583RFI, pp. 7 pgs). Lockheed Martin Advanced Technology Laboratories.
```
@techreport{Eaton2008Multimodal,
  author = {Eaton, Eric and Guo, Katherine and Hofmann, Martin},
  year = {2008},
  title = {Multimodal and Temporal Learning using Relational Networks},
  institution = {Lockheed Martin Advanced Technology Laboratories},
  number = {#DS-105-421-1583RFI},
  pages = {7 pgs},
  month = nov,
  group = {techreport},
  funding = {Lockheed Martin}
}
```

Conference Articles

Eaton, E., Holness, G., & McFarlane, D. (2009). Interactive Learning using Manifold Geometry. Proceedings of the AAAI Fall Symposium on Manifold Learning and Its Applications (AAAI Technical Report FS-09-04), 10–17.
```
@inproceedings{Eaton2009Interactive,
  author = {Eaton, Eric and Holness, Gary and McFarlane, Daniel},
  title = {Interactive Learning using Manifold Geometry},
  booktitle = {Proceedings of the AAAI Fall Symposium on Manifold Learning and Its Applications (AAAI Technical Report FS-09-04)},
  month = nov,
  location = {Arlington, VA},
  publisher = {AAAI Press},
  pages = {10--17},
  year = {2009},
  note = {Superseded by the AAAI-10 conference paper: Interactive Learning using Manifold Geometry.},
  group = {refereedworkshop},
  preprint = {Eaton2010Interactive.pdf},
  area = {InteractiveLearning},
  funding = {NSF, Lockheed Martin}
}
```

Eaton, E., & desJardins, M. (2009). Set-Based Boosting for Instance-level Transfer. Proceedings of the International Conference on Data Mining Workshop on Transfer Mining, 422–428.

@inproceedings{Eaton2009SetBased,
  author = {Eaton, Eric and desJardins, Marie},
  title = {Set-Based Boosting for Instance-level Transfer},
  booktitle = {Proceedings of the International Conference on Data Mining Workshop on Transfer Mining},
  location = {Miami, FL},
  publisher = {IEEE Press},
  pages = {422--428},
  month = dec,
  year = {2009},
  note = {Superseded by the AAAI-11 paper: Selective Transfer Between Learning Tasks Using Task-Based Boosting.},
  group = {refereedworkshop},
  preprint = {Eaton2009SetBased.pdf},
  area = {TransferLearning},
  funding = {NSF, Lockheed Martin}
}

  The success of transfer to improve learning on a
   target task is highly dependent on the selected source data.
   Instance-based transfer methods reuse data from the source
   tasks to augment the training data for the target task. If
   poorly chosen, this source data may inhibit learning, resulting
   in negative transfer. The current best performing algorithm
   for instance-based transfer, TrAdaBoost, performs poorly when
   given irrelevant source data.

   We present a novel set-based boosting technique for instance-based
   transfer. The proposed algorithm, TransferBoost, boosts
   both individual instances and collective sets of instances from
   each source task. In effect, TransferBoost boosts each source
   task, assigning higher weight to those source tasks which show
   positive transferability to the target task, and then adjusts
   the weights of the instances within each source task via
   AdaBoost. The results demonstrate that TransferBoost significantly
   improves transfer performance over existing instance-based
   algorithms when given a mix of relevant and irrelevant source data.

Eaton, E. (2008, July). Gridworld Search and Rescue: A Project Framework for a Course in Artificial Intelligence. Proceedings of the AAAI-08 AI Education Colloquium.

@inproceedings{Eaton2008Gridworld,
  author = {Eaton, Eric},
  title = {Gridworld Search and Rescue: A Project Framework for a Course in Artificial Intelligence},
  booktitle = {Proceedings of the AAAI-08 AI Education Colloquium},
  year = {2008},
  month = jul,
  address = {Chicago, IL},
  keywords = {education, project framework, search and rescue, gridworld},
  group = {refereedworkshop},
  preprint = {Eaton2008Gridworld.pdf},
  area = {Education},
  funding = {NSF,Other}
}

  This paper describes the Gridworld Search and Rescue simulator:
   freely available educational software that allows students
   to develop an intelligent agent for a search and rescue
   application in a partially observable gridworld. It permits students
   to focus on high-level AI issues for solving the problem
   rather than low-level robotic navigation. The complexity of
   the search and rescue problem supports a wide variety of solutions
   and AI techniques, including search, logical reasoning,
   planning, and machine learning, while the high-level GSAR
   simulator makes the complex problem manageable. The simulator
   represents a 2D disaster-stricken building for multiple
   rescue agents to explore and rescue autonomous injured victims.
   It was successfully used as the semester project for
   CMSC 471 (Artificial Intelligence) in Fall 2007 at UMBC.

Eaton, E., desJardins, M., & Lane, T. (2008). Modeling Transfer Relationships Between Learning Tasks for Improved Inductive Transfer. ecml08, 317–332.

@inproceedings{Eaton2008Modeling,
  author = {Eaton, Eric and desJardins, Marie and Lane, Terran},
  title = {Modeling Transfer Relationships Between Learning Tasks for Improved Inductive Transfer},
  booktitle = {ecml08},
  year = {2008},
  pages = {317--332},
  publisher = {Springer-Verlag},
  location = {Antwerp, Belgium},
  address = {Berlin, Heidelberg},
  note = {Acceptance rate: 20%},
  group = {journals},
  preprint = {Eaton2008Modeling.pdf},
  area = {TransferLearning},
  funding = {NSF}
}

  In this paper, we propose a novel graph-based method for
   knowledge transfer. We model the transfer relationships between source
   tasks by embedding the set of learned source models in a graph using
   transferability as the metric. Transfer to a new problem proceeds by
   mapping the problem into the graph, then learning a function on this
   graph that automatically determines the parameters to transfer to the
   new learning task. This method is analogous to inductive transfer along a
   manifold that captures the transfer relationships between the tasks. We
   demonstrate improved transfer performance using this method against
   existing approaches in several real-world domains.

Eaton, E., desJardins, M., & Lane, T. (2008, May). Using functions on a model graph for inductive transfer. Proceedings of the Northeast Student Colloquium on Artificial Intelligence (NESCAI-08).

@inproceedings{Eaton2008Using,
  author = {Eaton, Eric and desJardins, Marie and Lane, Terran},
  title = {Using functions on a model graph for inductive transfer},
  booktitle = {Proceedings of the Northeast Student Colloquium on Artificial Intelligence (NESCAI-08)},
  year = {2008},
  month = may,
  address = {Ithaca, NY},
  note = {Superseded by the ECML-08 paper: Modeling Transfer Relationships Between Learning Tasks for Improved Inductive Transfer.},
  group = {refereedworkshop},
  preprint = {Eaton2008Using.pdf},
  area = {TransferLearning},
  funding = {NSF}
}

  In this paper, we propose a novel graph-based
   method for knowledge transfer. We embed a set
   of learned background models in a graph that
   captures the transferability between the models.
   We then learn a function on this graph that automatically
   determines the parameters to transfer
   to each learning task. Transfer to a new problem
   proceeds by mapping the problem into the graph,
   then using the function to determine the parameters
   to transfer in learning the new model. This
   method is analogous to inductive transfer along a
   manifold that captures the transfer relationships
   between the tasks.

Eaton, E., desJardins, M., & Stevenson, J. (2007). Using multiresolution learning for transfer in image classification. Proceedings of the 22nd National Conference on Artificial Intelligence (AAAI).

@inproceedings{Eaton2007Using,
  author = {Eaton, Eric and desJardins, Marie and Stevenson, John},
  year = {2007},
  title = {Using multiresolution learning for transfer in image classification},
  booktitle = {Proceedings of the 22nd National Conference on Artificial Intelligence (AAAI)},
  address = {Vancouver, British Columbia, Canada},
  publisher = {AAAI Press},
  note = {[Student Abstract]},
  group = {refereedshort},
  preprint = {Eaton2007Using.pdf},
  poster = {poster-Eaton2007Using.pdf},
  area = {TransferLearning},
  funding = {NSF}
}

  Our work explores the transfer of knowledge at multiple
   levels of abstraction to improve learning. By exploiting
   the similarities between objects at various levels of detail,
   multiresolution learning can facilitate transfer between
   image classification tasks.

     We extract features from images at multiple levels of
   resolution, then use these features to create models
   at different resolutions. Upon receiving a new task,
   the closest-matching stored model can be generalized
   (adapted to the appropriate resolution) and transferred
   to the new task.

Eaton, E., & desJardins, M. (2006, June). Knowledge Transfer with a Multiresolution Ensemble of Classifiers. Proceedings of the ICML-06 Workshop on Structural Knowledge Transfer for Machine Learning.

@inproceedings{Eaton2006Knowledge,
  author = {Eaton, Eric and desJardins, Marie},
  title = {Knowledge Transfer with a Multiresolution Ensemble of Classifiers},
  booktitle = {Proceedings of the ICML-06 Workshop on Structural Knowledge Transfer for Machine Learning},
  year = {2006},
  address = {Pittsburgh, PA},
  month = jun,
  keywords = {knowledge transfer, multiresolution learning},
  group = {refereedworkshop},
  preprint = {Eaton2006Knowledge.pdf},
  area = {TransferLearning},
  funding = {NSF}
}

  We demonstrate transfer via an ensemble of
   classifiers, where each member focuses on one
   resolution of data. Lower-resolution ensemble
   members are shared between tasks, providing
   a medium for knowledge transfer.

desJardins, M., Eaton, E., & Wagstaff, K. (2006, June). Learning user preferences for sets of objects. icml06.

@inproceedings{desJardins2006Learning,
  author = {desJardins, Marie and Eaton, Eric and Wagstaff, Kiri},
  title = {Learning user preferences for sets of objects},
  booktitle = {icml06},
  year = {2006},
  month = jun,
  address = {Pittsburgh, PA},
  note = {Awarded recognition as a NASA Tech Brief in 2008},
  group = {journals},
  preprint = {desJardins2006Learning.pdf},
  area = {InteractiveLearning},
  funding = {NSF}
}

  Most work on preference learning has focused
   on pairwise preferences or rankings over individual
   items. In this paper, we present a
   method for learning preferences over sets of
   items. Our learning method takes as input a
   collection of positive examples–that is, one
   or more sets that have been identified by a
   user as desirable. Kernel density estimation
   is used to estimate the value function for individual
   items, and the desired set diversity is
   estimated from the average set diversity observed
   in the collection. Since this is a new
   learning problem, we introduce a new evaluation
   methodology and evaluate the learning
   method on two data collections: synthetic
   blocks-world data and a new real-world music
   data collection that we have gathered.

Eaton, E. (2006, July). Multi-Resolution Learning for Knowledge Transfer. Proceedings of the 21st National Conference on Artificial Intelligence (AAAI).

@inproceedings{Eaton2006MultiResolution,
  author = {Eaton, Eric},
  title = {Multi-Resolution Learning for Knowledge Transfer},
  booktitle = {Proceedings of the 21st National Conference on Artificial Intelligence (AAAI)},
  year = {2006},
  address = {Boston, MA},
  month = jul,
  publisher = {AAAI Press},
  note = {[Doctoral Consortium]},
  keywords = {knowledge transfer, multiresolution learning, aaai doctoral consortium},
  group = {refereedworkshop},
  preprint = {Eaton2006MultiResolution.pdf},
  area = {TransferLearning},
  funding = {NSF}
}

  Related objects may look similar at low-resolutions;
   differences begin to emerge naturally as the resolution
   is increased. By learning across multiple resolutions of
   input, knowledge can be transfered between related objects.
   My dissertation develops this idea and applies it
   to the problem of multitask transfer learning.

desJardins, M., Eaton, E., & Wagstaff, K. (2005). A context-sensitive and user-centric approach to developing personal assistants. Proceedings of the AAAI Spring Symposium on Persistent Assistants, 98–100.

@inproceedings{desJardins2005ContextSensitive,
  author = {desJardins, Marie and Eaton, Eric and Wagstaff, Kiri},
  title = {A context-sensitive and user-centric approach to developing personal assistants},
  booktitle = {Proceedings of the AAAI Spring Symposium on Persistent Assistants},
  year = {2005},
  month = mar,
  address = {Stanford, CA},
  pages = {98--100},
  group = {refereedworkshop},
  preprint = {desJardins2005ContextSensitive.pdf},
  area = {InteractiveLearning},
  funding = {NSF}
}

  Several ongoing projects in the MAPLE (Multi-Agent
   Planning and LEarning) lab at UMBC and the Machine
   Learning Systems Group at JPL focus on problems that
   we view as central to the development of persistent
   agents. This position paper describes our current research
   in this area, focusing on four topics in particular:
   effective use of observational and active learning,
   utilizing repeated behavioral contexts, clustering with
   annotated constraints, and learning user preferences.

Theses

Eaton, E. (2009). Selective Knowledge Transfer for Machine Learning [PhD thesis]. University of Maryland Baltimore County.

@phdthesis{Eaton2009Selective,
  author = {Eaton, Eric},
  title = {Selective Knowledge Transfer for Machine Learning},
  school = {University of Maryland Baltimore County},
  year = {2009},
  group = {dissertation},
  area = {TransferLearning},
  funding = {NSF, Other}
}

  Knowledge transfer from previously learned tasks to a new task is a fundamental component
   of human learning. Recent work has shown that knowledge transfer can also improve
   machine learning, enabling more rapid learning or higher levels of performance.
   Transfer allows learning algorithms to reuse knowledge from a set of previously learned
   source tasks to improve learning on new target tasks. Proper selection of the source
   knowledge to transfer to a given target task is critical to the success of knowledge transfer.
   Poorly chosen source knowledge may reduce the effectiveness of transfer, or hinder
   learning through a phenomenon known as negative transfer.

     This dissertation proposes several methods for source knowledge selection that are
   based on the transferability between learning tasks. Transferability is introduced as the
   change in performance on a target task between learning with and without transfer. These
   methods show that transferability can be used to select source knowledge for two major
   types of transfer: instance-based transfer, which reuses individual data instances from the
   source tasks, and model-based transfer, which transfers components of previously learned
   source models.

     For selective instance-based transfer, the proposed TransferBoost algorithm uses a
   novel form of set-based boosting to determine the individual source instances to transfer
   in learning the target task. TransferBoost reweights instances from each source task based
   on their collective transferability to the target task, and then performs regular boosting to
   adjust individual instance weights.

     For model-based transfer, the learning tasks are organized into a directed network
   based on their transfer relationships to each other. Tasks that are close in this network
   have high transferability, and tasks that are far apart have low transferability. Model-based
   transfer is equivalent to learning a labeling function on this network. This dissertation
   proposes the novel Spectral Graph Labeling algorithm that constrains the smoothness of
   the learned function using the graph’s Laplacian eigenvalues. This method is then applied
   to the task transferability network to learn a transfer function that automatically determines
   the model parameter values to transfer to a target task. Experiments validate the success
   of these methods for selective knowledge transfer, demonstrating significantly improved
   performance over existing methods.

Eaton, E. (2005). Clustering with Propagated Constraints [Master's thesis]. University of Maryland Baltimore County.

@mastersthesis{Eaton2005MastersThesis,
  author = {Eaton, Eric},
  title = {Clustering with Propagated Constraints},
  school = {University of Maryland Baltimore County},
  year = {2005},
  group = {dissertation},
  preprint = {Eaton2005MastersThesis.pdf},
  area = {ConstClustering},
  funding = {NSF}
}

  Background knowledge in the form of constraints can dramatically improve the quality
   of generated clustering models. In constrained clustering, these constraints typically
   specify the relative cluster membership of pairs of points. They are tedious to specify and
   expensive from a user perspective, yet are very useful in large quantities. Existing constrained
   clustering methods perform well when given large quantities of constraints, but do
   not focus on performing well when given very small quantities.

     This thesis focuses on providing a high-quality clustering with small quantities of
   constraints. It proposes a method for propagating pairwise constraints to nearby instances
   using a Gaussian function. This method takes a few easily specified constraints, and propagates
   them to nearby pairs of points to constrain the local neighborhood. Clustering with
   these propagated constraints can yield superior performance with fewer constraints than
   clustering with only the original user-specified constraints. The experiments compare the
   performance of clustering with propagated constraints to that of established constrained
   clustering algorithms on several real-world data sets.

Miscellaneous

Eaton, E. (2008). Gridworld Search and Rescue Software. Available online at: http://maple.cs.umbc.edu/ ericeaton/searchandrescue/.
```
@misc{Eaton2008GridworldSoftware,
  author = {Eaton, Eric},
  year = {2008},
  title = {Gridworld Search and Rescue Software},
  howpublished = {Available online at: http://maple.cs.umbc.edu/~ericeaton/searchandrescue/},
  group = {software},
  area = {Education},
  funding = {NSF, Other}
}
```
Eaton, E., desJardins, M., & Wagstaff, K. (2006). DDPref Software: Learning preferences for sets of objects. Available online at: http://maple.cs.umbc.edu/ ericeaton/software/DDPref.zip.
```
@misc{Eaton2006DDPrefSoftware,
  author = {Eaton, Eric and desJardins, Marie and Wagstaff, Kiri},
  year = {2006},
  title = {DDPref Software: Learning preferences for sets of objects},
  howpublished = {Available online at: http://maple.cs.umbc.edu/~ericeaton/software/DDPref.zip},
  group = {software},
  link = {http://maple.cs.umbc.edu/~ericeaton/software/DDPref.zip},
  area = {InteractiveLearning},
  funding = {NSF}
}
```