Lifelong Learning and Multi-task Learning

AUTHORS TITLE YEAR VENUE / PUBLISHER
Jorge Mendez & Eric Eaton Lifelong learning of compositional structures 2021 International Conference on Learning Representations
 
Seungwon Lee, Sima Behpour, & Eric Eaton Sharing less is more: Lifelong learning in deep networks with selective layer transfer 2020 Lifelong Learning Workshop at ICML
 
Jorge Mendez, Boyu Wang, & Eric Eaton Lifelong policy gradient learning of factored policies for faster training without forgetting
Earlier version was awarded best paper at the ICML'20 Workshop on Lifelong Learning
2020 Advances in Neural Information Processing Systems
 
Jorge Mendez & Eric Eaton A general framework for continual learning of compositional structures
Superseded by the ICLR-21 paper Lifelong learning of compositional structures.
2020 Continual Learning Workshop at ICML
 
Mohammad Rostami, David Isele, & Eric Eaton Using task descriptions in lifelong machine learning for improved performance and zero-shot transfer 2020 Journal of Artificial Intelligence Research
 
Seungwon Lee, James Stokes, & Eric Eaton Learning shared knowledge for deep lifelong learning using deconvolutional networks 2019 Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19)
 
David Isele, Eric Eaton, Mark Roberts, & David Aha Modeling consecutive task learning with task graph agendas 2018 Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-18)
 
Jorge A. Mendez, Shashank Shivkumar, & Eric Eaton Lifelong inverse reinforcement learning 2018 Neural Information Processing Systems
 
Mohammad Rostami, Soheil Kolouri, Kyungnam Kim, & Eric Eaton Multi-agent distributed lifelong learning for collective knowledge acquisition 2018 Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-18)
 
Decebal Constantin Mocanu, Haitham Bou Ammar, Luis Puig, Eric Eaton, & Antonio Liotta Estimating 3D trajectories from 2D projections via disjunctive factored four-way conditional restricted Boltzmann machines 2017 Pattern Recognition
 
Christopher Clingerman & Eric Eaton Lifelong machine learning with Gaussian processes 2017 European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD-17)
 
David Isele, Jose Marcio Luna, Eric Eaton, Gabriel V. de la Cruz, James Irwin, Brandon Kallaher, & Matthew E. Taylor Lifelong Learning for Disturbance Rejection on Mobile Robots 2016 International Conference on Intelligent Robots and Systems (IROS-16)
 
David Isele, Mohammad Rostami, & Eric Eaton Using task features for zero-shot knowledge transfer in lifelong learning
Awarded sole IJCAI-16 Distinguished Student Paper
2016 International Joint Conference on Artificial Intelligence (IJCAI-16)
 
David Isele, Jose Marcio Luna, Eric Eaton, Gabriel V. de la Cruz, James Irwin, Brandon Kallaher, & Matthew E. Taylor Work in Progress: Lifelong Learning for Disturbance Rejection on Mobile Robots
Superseded by the IROS-16 paper Lifelong Learning for Disturbance Rejection on Mobile Robots.
2016 AAMAS'16 Workshop on Adaptive Learning Agents
 
Haitham Bou Ammar, Rasul Tutunov, & Eric Eaton Safe policy search for lifelong reinforcement learning with sublinear regret 2015 International Conference on Machine Learning (ICML-15)
 
Haitham Bou Ammar, Eric Eaton, Jose Marcio Luna, & Paul Ruvolo Autonomous cross-domain knowledge transfer in lifelong policy gradient reinforcement learning
Finalist for IJCAI-15 Distinguished Paper award
2015 International Joint Conference on Artificial Intelligence (IJCAI-15)
 
Paul Ruvolo & Eric Eaton Online Multi-Task Learning via Sparse Dictionary Optimization 2014 AAAI Conference on Artificial Intelligence (AAAI-14)
 
Vishnu Purushothaman Sreenivasan, Haitham Bou Ammar, & Eric Eaton Online Multi-Task Gradient Temporal-Difference Learning 2014 AAAI Conference on Artificial Intelligence (AAAI-14)
 
Haitham Bou Ammar, Eric Eaton, Paul Ruvolo, & Matthew E. Taylor Online Multi-Task Learning for Policy Gradient Methods 2014 International Conference on Machine Learning (ICML-14)
 
Paul Ruvolo & Eric Eaton Active Task Selection for Lifelong Machine Learning 2013 AAAI Conference on Artificial Intelligence (AAAI-13)
 
Paul Ruvolo & Eric Eaton ELLA: An Efficient Lifelong Learning Algorithm 2013 International Conference on Machine Learning (ICML-13)
 
Paul Ruvolo & Eric Eaton Online Multi-Task Learning based on K-SVD
Superseded by the AAAI-14 paper Online Multi-Task Learning via Sparse Dictionary Optimization.
2013 ICML 2013 Workshop on Theoretically Grounded Transfer Learning
 
Eric Eaton (chair) Lifelong Machine Learning: Proceedings of the 2013 AAAI Spring Symposium 2013 AAAI Press
 
Paul Ruvolo & Eric Eaton Scalable Lifelong Learning with Active Task Selection
Superseded by the AAAI-13 paper Active Task Selection for Lifelong Machine Learning.
2013 AAAI 2013 Spring Symposium on Lifelong Machine Learning
 

Transfer Learning

AUTHORS TITLE YEAR VENUE / PUBLISHER
Mohammad Rostami, Soheil Kolouri, Eric Eaton, & Kyungnam Kim SAR image classification using few-shot cross-domain transfer learning 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops
 
Mohammad Rostami, Soheil Kolouri, Eric Eaton, & Kyungnam Kim Deep transfer learning for few-shot SAR image classification 2019 Remote Sensing
 
Boyu Wang, Jorge Mendez, Mingbo Cai, & Eric Eaton Transfer learning via minimizing the performance gap between domains 2019 Advances in Neural Information Processing Systems
 
Haitham Bou Ammar, Eric Eaton, Paul Ruvolo, & Matthew E. Taylor Unsupervised cross-domain transfer in policy gradient reinforcement learning via manifold alignment 2015 AAAI Conference on Artificial Intelligence (AAAI-15)
 
Haitham Bou Ammar, Eric Eaton, Matthew E. Taylor, Decebal Mocanu, Kurt Driessens, Gerhard Weiss, & Karl Tuyls An automated measure of MDP similarity for transfer in reinforcement learning 2014 AAAI'14 Workshop on Machine Learning for Interactive Systems
 
Diane Oyen, Eric Eaton, & Terran Lane Inferring tasks for improved network structure discovery 2012 Snowbird Learning Workshop
 
Eric Eaton & Marie desJardins Selective Transfer Between Learning Tasks Using Task-Based Boosting 2011 AAAI Conference on Artificial Intelligence (AAAI-11)
 
Eric Eaton & Terran Lane The Importance of Selective Knowledge Transfer for Lifelong Learning 2011 AAAI-11 Workshop on Lifelong Learning from Sensorimotor Data
 
Eric Eaton & Marie desJardins Set-Based Boosting for Instance-level Transfer
Superseded by the AAAI-11 paper Selective Transfer Between Learning Tasks Using Task-Based Boosting.
2009 International Conference on Data Mining Workshop on Transfer Mining
 
Eric Eaton Selective Knowledge Transfer for Machine Learning 2009 Ph.D. Thesis, University of Maryland Baltimore County
 
Eric Eaton, Marie desJardins, & Terran Lane Using functions on a model graph for inductive transfer
Superseded by the ECML-08 paper Modeling Transfer Relationships Between Learning Tasks for Improved Inductive Transfer.
2008 Northeast Student Colloquium on Artificial Intelligence (NESCAI-08)
 
Eric Eaton, Marie desJardins, & Terran Lane Modeling Transfer Relationships Between Learning Tasks for Improved Inductive Transfer 2008 European Conference on Machine Learning (ECML)
 
Eric Eaton, Marie desJardins, & John Stevenson Using multiresolution learning for transfer in image classification 2007 National Conference on Artificial Intelligence (AAAI)
 
Eric Eaton Multi-Resolution Learning for Knowledge Transfer 2006 National Conference on Artificial Intelligence (AAAI)
 
Eric Eaton & Marie desJardins Knowledge Transfer with a Multiresolution Ensemble of Classifiers 2006 ICML-06 Workshop on Structural Knowledge Transfer for Machine Learning
 

Interactive and Interpretable Learning

AUTHORS TITLE YEAR VENUE / PUBLISHER
Efstathios D. Gennatas, Jerome H. Friedman, Lyle H. Ungar, Romain Pirracchio, Eric Eaton, Lara G. Reichmann, Yannet Interian, Jose Marcio Luna, Charles B. Simone, Andrew Auerbach, Elier Delgado, Mark J. van der Laan, Timothy D. Solberg, & Gilmer Valdes Expert-augmented machine learning 2020 Proceedings of the National Academy of Sciences 117(9)
 
Jose Marcio Luna, Efstathios D. Gennatas, Lyle H. Ungar, Eric Eaton, Eric S. Diffenderfer, Shane T. Jensen, Charles B. Simone, Jerome H. Friedman, Timothy D. Solberg, & Gilmer Valdes Building more accurate decision trees with the additive tree 2019 Proceedings of the National Academy of Sciences 116(40)
 
Kiri Wagstaff, Marie desJardins, & Eric Eaton Modeling and learning user preferences over sets 2010 Journal of Experimental & Theoretical Artificial Intelligence 22(3)
 
Eric Eaton, Gary Holness, & Daniel McFarlane Interactive Learning using Manifold Geometry 2010 AAAI Conference on Artificial Intelligence (AAAI-10)
 
Eric Eaton, Gary Holness, & Daniel McFarlane Interactive Learning using Manifold Geometry
Superseded by the AAAI-10 conference paper Interactive Learning using Manifold Geometry.
2009 AAAI Fall Symposium on Manifold Learning and Its Applications (AAAI Technical Report FS-09-04)
 
Marie desJardins, Eric Eaton, & Kiri Wagstaff Learning user preferences for sets of objects
Awarded recognition as a NASA Tech Brief in 2008
2006 International Conference on Machine Learning (ICML)
 
Eric Eaton, Marie desJardins, & Kiri Wagstaff DDPref Software: Learning preferences for sets of objects 2006 Available online at: http://maple.cs.umbc.edu/ ericeaton/software/DDPref.zip
 
Marie desJardins, Eric Eaton, & Kiri Wagstaff A context-sensitive and user-centric approach to developing personal assistants 2005 AAAI Spring Symposium on Persistent Assistants
 

Constrained Clustering

AUTHORS TITLE YEAR VENUE / PUBLISHER
Eric Eaton, Marie desJardins, & Sara Jacob Multi-view constrained clustering with an incomplete mapping between views 2014 Knowledge and Information Systems 38(1)
 
Eric Eaton, Marie desJardins, & Sara Jacob Multi-View Clustering with Constraint Propagation for Learning with an Incomplete Mapping Between Views 2010 Conference on Information and Knowledge Management (CIKM'10)
 
Eric Eaton Clustering with Propagated Constraints 2005 Master's Thesis, University of Maryland Baltimore County
 

Relational Network Analysis

AUTHORS TITLE YEAR VENUE / PUBLISHER
Rasul Tutunov, Haitham Bou Ammar, Ali Jadbabaie, & Eric Eaton On the degree distribution of Pólya urn graph processes 2014 arXiv:1410.8515 Preprint
 
Eric Eaton & Rachael Mansbach A spin-glass model for semi-supervised community detection 2012 AAAI Conference on Artificial Intelligence (AAAI-12)
 

Computational Sustainability

AUTHORS TITLE YEAR VENUE / PUBLISHER
Pengyuan Shen, William Braham, Yun Kyu Yi, & Eric Eaton Rapid multi-objective optimization with multi-year future weather condition and decision-making support for building retrofit 2019 Energy
 
Eric Eaton, Carla Gomes, & Brian Williams, editors Special Issue of AI Magazine on Computational Sustainability 2014 AAAI Press
 
Eric Eaton, Carla Gomes, & Brian Williams Computational Sustainability 2014 AI Magazine 35(2)
 

Medicine

AUTHORS TITLE YEAR VENUE / PUBLISHER
Efstathios D. Gennatas, Jerome H. Friedman, Lyle H. Ungar, Romain Pirracchio, Eric Eaton, Lara G. Reichmann, Yannet Interian, Jose Marcio Luna, Charles B. Simone, Andrew Auerbach, Elier Delgado, Mark J. van der Laan, Timothy D. Solberg, & Gilmer Valdes Expert-augmented machine learning 2020 Proceedings of the National Academy of Sciences 117(9)
 
Jose Marcio Luna, Efstathios D. Gennatas, Lyle H. Ungar, Eric Eaton, Eric S. Diffenderfer, Shane T. Jensen, Charles B. Simone, Jerome H. Friedman, Timothy D. Solberg, & Gilmer Valdes Building more accurate decision trees with the additive tree 2019 Proceedings of the National Academy of Sciences 116(40)
 
Julia E. Reid & Eric Eaton Artificial intelligence for pediatric ophthalmology 2019 Current Opinion in Ophthalmology 30(5)
 

Education

AUTHORS TITLE YEAR VENUE / PUBLISHER
Eric Eaton A lightweight approach to academic research group management using online tools: Spend more time on research and less on management 2019 Educational Advances in Artificial Intelligene (EAAI) Symposium
 
Eric Eaton Teaching integrated AI through interdisciplinary project-driven courses 2017 AI Magazine 38(2)
 
Douglas Fisher, Bistra Dilkina, Eric Eaton, & Carla Gomes Incorporating computational sustainability into AI education through a freely-available, collectively-composed supplementary lab text 2012 AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-12)
 
Douglas Fisher, Bistra Dilkina, Eric Eaton, & Carla Gomes Incorporating computational sustainability into AI education through a freely-available, collectively-composed supplementary lab text [Oral Presentation] 2012 International Conference on Computational Sustainability (CompSust'12)
 
Eric Eaton Gridworld Search and Rescue: A Project Framework for a Course in Artificial Intelligence 2008 AAAI-08 AI Education Colloquium
 
Eric Eaton Gridworld Search and Rescue Software 2008 Available online at: http://maple.cs.umbc.edu/ ericeaton/searchandrescue/
 

Other

AUTHORS TITLE YEAR VENUE / PUBLISHER
Gilmer Valdes, Jose Marcio Luna, Eric Eaton, Charles B. Simone II, Lyle H. Ungar, & Timothy D. Solberg MediBoost: a Patient Stratification Tool for Interpretable Decision Making in the Era of Precision Medicine 2016 Scientific Reports
 
Eric Eaton, Caio Mucchiani, Mayumi Mohan, David Isele, Jose Marcio Luna, & Christopher Clingerman Design of a low-cost platform for autonomous mobile service robots 2016 IJCAI-16 Workshop on Autonomous Mobile Service Robots
 

Unspecified

AUTHORS TITLE YEAR VENUE / PUBLISHER
Eric Eaton, Dan McFarlane, & Martin Hofmann Analysis of Complex Data Using Heterogeneous Relational Models 2009 Technical Report #DS-104-421-1610WP, Lockheed Martin Advanced Technology Laboratories
 
Eric Eaton, Gary Holness, & Dan McFarlane Situational Awareness through Interactive Learning 2009 Technical Report #DS-104-421-1607WP, Lockheed Martin Advanced Technology Laboratories
 
Meghann Lomas, Daniel McFarlane, Eric Eaton, Robert Szczerba, & Jerry Franke Dynamic Ensemble Planning for Tactical Hierarchies 2009 Technical Report #DS-104-421-1604WP, Lockheed Martin Advanced Technology Laboratories
 
Eric Eaton, Katherine Guo, & Martin Hofmann Predicting and Verifying Effects of Cyber Operations from Indirect Observations 2009 Technical Report #DS-105-421-1598WP, Lockheed Martin Advanced Technology Laboratories
 
Eric Eaton, Katherine Guo, & Martin Hofmann Multimodal and Temporal Learning using Relational Networks 2008 Technical Report #DS-105-421-1583RFI, Lockheed Martin Advanced Technology Laboratories