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2020 – today
- 2024
[j29]Andrea Soltoggio
, Eseoghene Ben-Iwhiwhu, Vladimir Braverman, Eric Eaton, Benjamin Epstein, Yunhao Ge, Lucy Halperin, Jonathan P. How, Laurent Itti, Michael A. Jacobs, Pavan Kantharaju, Long Le, Steven Lee, Xinran Liu, Sildomar T. Monteiro, David Musliner, Saptarshi Nath, Priyadarshini Panda, Christos Peridis, Hamed Pirsiavash, Vishwa S. Parekh, Kaushik Roy, Shahaf S. Shperberg, Hava T. Siegelmann, Peter Stone, Kyle Vedder, Jingfeng Wu, Lin Yang, Guangyao Zheng, Soheil Kolouri:
A collective AI via lifelong learning and sharing at the edge. Nat. Mac. Intell. 6(3): 251-264 (2024)- [c48]Eric Eaton, Susan L. Epstein:
Artificial Intelligence in the CS2023 Undergraduate Computer Science Curriculum: Rationale and Challenges. AAAI 2024: 23078-23083 - [c47]Kyle Vedder, Neehar Peri, Nathaniel Chodosh, Ishan Khatri, Eric Eaton, Dinesh Jayaraman, Yang Liu, Deva Ramanan, James Hays:
ZeroFlow: Scalable Scene Flow via Distillation. ICLR 2024 - [c46]Meghna Gummadi, Cassandra Kent, Karl Schmeckpeper, Eric Eaton:
A Metacognitive Approach to Out-of-Distribution Detection for Segmentation. ICRA 2024: 6642-6649 - [i36]Marcel Hussing, Claas Voelcker, Igor Gilitschenski, Amir-massoud Farahmand, Eric Eaton:
Dissecting Deep RL with High Update Ratios: Combatting Value Overestimation and Divergence. CoRR abs/2403.05996 (2024) - [i35]Long Le, Marcel Hussing, Eric Eaton:
Distributed Continual Learning. CoRR abs/2405.17466 (2024) - [i34]Guiqiu Liao, Matjaz Jogan, Sai Koushik, Eric Eaton, Daniel A. Hashimoto:
Disentangling spatio-temporal knowledge for weakly supervised object detection and segmentation in surgical video. CoRR abs/2407.15794 (2024) - [i33]Jean Park, Kuk Jin Jang, Basam Alasaly, Sriharsha Mopidevi, Andrew Zolensky, Eric Eaton, Insup Lee, Kevin Johnson:
Assessing Modality Bias in Video Question Answering Benchmarks with Multimodal Large Language Models. CoRR abs/2408.12763 (2024) - 2023
- [j28]Boyu Wang, Jorge A. Mendez, Changjian Shui, Fan Zhou, Di Wu, Gezheng Xu, Christian Gagné, Eric Eaton:
Gap Minimization for Knowledge Sharing and Transfer. J. Mach. Learn. Res. 24: 33:1-33:57 (2023) - [j27]Megan M. Baker, Alexander New, Mario Aguilar-Simon, Ziad Al-Halah, Sébastien M. R. Arnold, Eseoghene Ben-Iwhiwhu, Andrew P. Brna, Ethan Brooks, Ryan C. Brown, Zachary Daniels, Anurag Reddy Daram, Fabien Delattre, Ryan Dellana, Eric Eaton, Haotian Fu, Kristen Grauman, Jesse Hostetler, Shariq Iqbal, Cassandra Kent, Nicholas Ketz, Soheil Kolouri, George Konidaris, Dhireesha Kudithipudi, Erik G. Learned-Miller, Seungwon Lee, Michael Littman, Sandeep Madireddy, Jorge A. Mendez, Eric Q. Nguyen, Christine D. Piatko, Praveen K. Pilly, Aswin Raghavan, Abrar Rahman, Santhosh Kumar Ramakrishnan, Neale Ratzlaff, Andrea Soltoggio, Peter Stone, Indranil Sur, Zhipeng Tang, Saket Tiwari, Kyle Vedder, Felix Wang, Zifan Xu, Angel Yanguas-Gil, Harel Yedidsion, Shangqun Yu, Gautam K. Vallabha:
A domain-agnostic approach for characterization of lifelong learning systems. Neural Networks 160: 274-296 (2023) - [j26]Jorge A. Mendez, Eric Eaton:
How to Reuse and Compose Knowledge for a Lifetime of Tasks: A Survey on Continual Learning and Functional Composition. Trans. Mach. Learn. Res. 2023 (2023) - [c45]Samantha Haines, Eric Eaton, Md Liakat Ali:
Machine Learning Models for Histopathological Breast Cancer Image Classification. AIIoT 2023: 36-41 - [c44]Ashwin De Silva, Rahul Ramesh, Lyle H. Ungar, Marshall G. Hussain Shuler, Noah J. Cowan, Michael Platt, Chen Li, Leyla Isik, Seung-Eon Roh, Adam Charles, Archana Venkataraman, Brian Caffo, Javier J. How, Justus M. Kebschull, John W. Krakauer, Maxim Bichuch, Kaleab Alemayehu Kinfu, Eva Yezerets, Dinesh Jayaraman, Jong M. Shin, Soledad Villar, Ian Phillips, Carey E. Priebe, Thomas Hartung, Michael I. Miller, Jayanta Dey, Ningyuan Huang, Eric Eaton, Ralph Etienne-Cummings, Elizabeth L. Ogburn, Randal C. Burns, Onyema Osuagwu, Brett Mensh, Alysson R. Muotri, Julia Brown, Chris White, Weiwei Yang, Andrei A. Rusu, Timothy D. Verstynen, Konrad P. Kording, Pratik Chaudhari, Joshua T. Vogelstein:
Prospective Learning: Principled Extrapolation to the Future. CoLLAs 2023: 347-357 - [c43]Duo Lu, Eric Eaton, Matt Weg, Wei Wang, Steven Como, Jeffrey Wishart, Hongbin Yu, Yezhou Yang:
CAROM Air - Vehicle Localization and Traffic Scene Reconstruction from Aerial Videos. ICRA 2023: 10666-10673 - [c42]Christian Servin, Brett A. Becker, Eric Eaton, Amruth N. Kumar:
Fuzzy Logic++: Towards Developing Fuzzy Education Curricula Using ACM/IEEE/AAAI CS2023. NAFIPS 2023: 184-193 - [c41]Eric Eaton, Marcel Hussing, Michael Kearns, Jessica Sorrell:
Replicable Reinforcement Learning. NeurIPS 2023 - [i32]Megan M. Baker, Alexander New, Mario Aguilar-Simon, Ziad Al-Halah, Sébastien M. R. Arnold, Eseoghene Ben-Iwhiwhu, Andrew P. Brna, Ethan Brooks, Ryan C. Brown, Zachary Daniels, Anurag Reddy Daram, Fabien Delattre, Ryan Dellana, Eric Eaton, Haotian Fu, Kristen Grauman, Jesse Hostetler, Shariq Iqbal, Cassandra Kent, Nicholas Ketz, Soheil Kolouri, George Dimitri Konidaris, Dhireesha Kudithipudi, Erik G. Learned-Miller, Seungwon Lee, Michael Littman, Sandeep Madireddy, Jorge A. Mendez, Eric Q. Nguyen, Christine D. Piatko, Praveen K. Pilly, Aswin Raghavan, Abrar Rahman, Santhosh Kumar Ramakrishnan, Neale Ratzlaff, Andrea Soltoggio, Peter Stone, Indranil Sur, Zhipeng Tang, Saket Tiwari, Kyle Vedder, Felix Wang, Zifan Xu, Angel Yanguas-Gil, Harel Yedidsion, Shangqun Yu, Gautam K. Vallabha:
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems. CoRR abs/2301.07799 (2023) - [i31]Kyle Vedder, Neehar Peri, Nathaniel Chodosh, Ishan Khatri, Eric Eaton, Dinesh Jayaraman, Yang Liu, Deva Ramanan, James Hays:
ZeroFlow: Fast Zero Label Scene Flow via Distillation. CoRR abs/2305.10424 (2023) - [i30]Pengyuan Lu, Michele Caprio, Eric Eaton, Insup Lee:
Zero-shot Task Preference Addressing Enabled by Imprecise Bayesian Continual Learning. CoRR abs/2305.14782 (2023) - [i29]Eric Eaton, Marcel Hussing, Michael Kearns, Jessica Sorrell:
Replicable Reinforcement Learning. CoRR abs/2305.15284 (2023) - [i28]Duo Lu, Eric Eaton, Matt Weg, Wei Wang, Steven Como, Jeffrey Wishart, Hongbin Yu, Yezhou Yang:
CAROM Air - Vehicle Localization and Traffic Scene Reconstruction from Aerial Videos. CoRR abs/2306.00075 (2023) - [i27]Marcel Hussing, Jorge A. Mendez, Anisha Singrodia, Cassandra Kent, Eric Eaton:
Robotic Manipulation Datasets for Offline Compositional Reinforcement Learning. CoRR abs/2307.07091 (2023) - [i26]Meghna Gummadi, Cassandra Kent, Karl Schmeckpeper, Eric Eaton:
A Metacognitive Approach to Out-of-Distribution Detection for Segmentation. CoRR abs/2311.07578 (2023) - 2022
- [c40]Jorge A. Mendez, Marcel Hussing, Meghna Gummadi, Eric Eaton:
CompoSuite: A Compositional Reinforcement Learning Benchmark. CoLLAs 2022: 982-1003 - [c39]Meghna Gummadi, David Kent, Jorge A. Mendez, Eric Eaton:
SHELS: Exclusive Feature Sets for Novelty Detection and Continual Learning Without Class Boundaries. CoLLAs 2022: 1065-1085 - [c38]Jorge A. Mendez, Harm van Seijen, Eric Eaton:
Modular Lifelong Reinforcement Learning via Neural Composition. ICLR 2022 - [c37]Kyle Vedder, Eric Eaton:
Sparse PointPillars: Maintaining and Exploiting Input Sparsity to Improve Runtime on Embedded Systems. IROS 2022: 2025-2031 - [i25]Joshua T. Vogelstein, Timothy D. Verstynen, Konrad P. Kording, Leyla Isik, John W. Krakauer, Ralph Etienne-Cummings, Elizabeth L. Ogburn, Carey E. Priebe, Randal C. Burns, Kwame S. Kutten, James J. Knierim, James B. Potash, Thomas Hartung, Lena Smirnova, Paul Worley, Alena V. Savonenko, Ian Phillips, Michael I. Miller, René Vidal, Jeremias Sulam, Adam Charles, Noah J. Cowan, Maxim Bichuch, Archana Venkataraman, Chen Li, Nitish V. Thakor, Justus M. Kebschull, Marilyn S. Albert, Jinchong Xu, Marshall G. Hussain Shuler, Brian Caffo, J. Tilak Ratnanather, Ali Geisa, Seung-Eon Roh, Eva Yezerets, Meghana Madhyastha, Javier J. How, Tyler M. Tomita, Jayanta Dey, Ningyuan Huang, Jong M. Shin, Kaleab Alemayehu Kinfu, Pratik Chaudhari, Ben Baker, Anna Schapiro, Dinesh Jayaraman, Eric Eaton, Michael Platt, Lyle H. Ungar, Leila Wehbe, Ádám Kepecs, Amy Christensen, Onyema Osuagwu, Bing Brunton, Brett Mensh, Alysson R. Muotri, Gabriel A. Silva, Francesca Puppo, Florian Engert, Elizabeth Hillman, Julia Brown, Chris White, Weiwei Yang:
Prospective Learning: Back to the Future. CoRR abs/2201.07372 (2022) - [i24]Boyu Wang, Jorge A. Mendez, Changjian Shui, Fan Zhou, Di Wu, Christian Gagné, Eric Eaton:
Gap Minimization for Knowledge Sharing and Transfer. CoRR abs/2201.11231 (2022) - [i23]Meghna Gummadi, David Kent, Jorge A. Mendez, Eric Eaton:
SHELS: Exclusive Feature Sets for Novelty Detection and Continual Learning Without Class Boundaries. CoRR abs/2206.13720 (2022) - [i22]Jorge A. Mendez, Harm van Seijen, Eric Eaton:
Modular Lifelong Reinforcement Learning via Neural Composition. CoRR abs/2207.00429 (2022) - [i21]Jorge A. Mendez, Shashank Shivkumar, Eric Eaton:
Lifelong Inverse Reinforcement Learning. CoRR abs/2207.00461 (2022) - [i20]Jorge A. Mendez, Marcel Hussing, Meghna Gummadi, Eric Eaton:
CompoSuite: A Compositional Reinforcement Learning Benchmark. CoRR abs/2207.04136 (2022) - [i19]Jorge A. Mendez, Eric Eaton:
How to Reuse and Compose Knowledge for a Lifetime of Tasks: A Survey on Continual Learning and Functional Composition. CoRR abs/2207.07730 (2022) - [i18]Marcel Hussing, Karen Li, Eric Eaton:
Land Use Prediction using Electro-Optical to SAR Few-Shot Transfer Learning. CoRR abs/2212.03084 (2022) - 2021
- [c36]Jorge A. Mendez, Eric Eaton:
Lifelong Learning of Compositional Structures. ICLR 2021 - [c35]Seungwon Lee, Sima Behpour, Eric Eaton:
Sharing Less is More: Lifelong Learning in Deep Networks with Selective Layer Transfer. ICML 2021: 6065-6075 - [i17]Kyle Vedder, Eric Eaton:
Sparse PointPillars: Exploiting Sparsity in Birds-Eye-View Object Detection. CoRR abs/2106.06882 (2021) - [i16]Ali Geisa, Ronak D. Mehta, Hayden S. Helm, Jayanta Dey, Eric Eaton, Carey E. Priebe, Joshua T. Vogelstein:
Towards a theory of out-of-distribution learning. CoRR abs/2109.14501 (2021) - 2020
- [j25]Mohammad Rostami, David Isele, Eric Eaton:
Using Task Descriptions in Lifelong Machine Learning for Improved Performance and Zero-Shot Transfer. J. Artif. Intell. Res. 67: 673-704 (2020) - [c34]Jorge A. Mendez, Boyu Wang, Eric Eaton:
Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting. NeurIPS 2020 - [i15]Jorge A. Mendez, Boyu Wang, Eric Eaton:
Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting. CoRR abs/2007.07011 (2020) - [i14]Jorge A. Mendez, Eric Eaton:
Lifelong Learning of Compositional Structures. CoRR abs/2007.07732 (2020)
2010 – 2019
- 2019
- [j24]Mohammad Rostami, Soheil Kolouri, Eric Eaton, Kyungnam Kim:
Deep Transfer Learning for Few-Shot SAR Image Classification. Remote. Sens. 11(11): 1374 (2019) - [c33]Eric Eaton:
A Lightweight Approach to Academic Research Group Management Using Online Tools: Spend More Time on Research and Less on Management. AAAI 2019: 9644-9647 - [c32]Mohammad Rostami, Soheil Kolouri, Eric Eaton, Kyungnam Kim:
SAR Image Classification Using Few-Shot Cross-Domain Transfer Learning. CVPR Workshops 2019: 907-915 - [c31]Seungwon Lee, James Stokes, Eric Eaton:
Learning Shared Knowledge for Deep Lifelong Learning using Deconvolutional Networks. IJCAI 2019: 2837-2844 - [c30]Boyu Wang, Jorge A. Mendez, Mingbo Cai, Eric Eaton:
Transfer Learning via Minimizing the Performance Gap Between Domains. NeurIPS 2019: 10644-10654 - [i13]Efstathios D. Gennatas, Jerome H. Friedman, Lyle H. Ungar, Romain Pirracchio, Eric Eaton, L. Reichman, Yannet Interian, Charles B. Simone II, A. Auerbach, E. Delgado, Mark J. van der Laan, Timothy D. Solberg, Gilmer Valdes:
Expert-Augmented Machine Learning. CoRR abs/1903.09731 (2019) - [i12]Julia E. Reid, Eric Eaton:
Artificial Intelligence for Pediatric Ophthalmology. CoRR abs/1904.08796 (2019) - [i11]Mohammad Rostami, Soheil Kolouri, Zak Murez, Yuri Owekcho, Eric Eaton, Kyungnam Kim:
Zero-Shot Image Classification Using Coupled Dictionary Embedding. CoRR abs/1906.10509 (2019) - 2018
- [j23]Sheila A. McIlraith, Kilian Q. Weinberger, G. Michael Youngblood, Karen L. Myers, Eric Eaton, Michael Wollowski:
A Recap of the AAAI and IAAI 2018 Conferences and the EAAI Symposium. AI Mag. 39(4): 3-16 (2018) - [j22]Eric Eaton, Amy McGovern:
Welcome to AI matters, volume 3, issue 4. AI Matters 3(4): 3 (2018) - [j21]Amy McGovern, Eric Eaton:
An interview with Ayanna Howard. AI Matters 3(4): 5-7 (2018) - [j20]Eric Eaton, Sven Koenig, Claudia Schulz, Francesco Maurelli, John S. Y. Lee, Joshua Eckroth, Mark Crowley, Richard G. Freedman, Rogelio Enrique Cardona-Rivera, Tiago Machado, Tom Williams:
Blue sky ideas in artificial intelligence education from the EAAI 2017 new and future AI educator program. AI Matters 3(4): 23-31 (2018) - [j19]Amy McGovern, Eric Eaton:
Welcome to AI matters 4(1). AI Matters 4(1): 3-4 (2018) - [c29]Mohammad Rostami, Eric Eaton:
Lifelong Learning Networks: Beyond Single Agent Lifelong Learning. AAAI 2018: 8145-8146 - [c28]Mohammad Rostami, Soheil Kolouri, Kyungnam Kim, Eric Eaton:
Multi-Agent Distributed Lifelong Learning for Collective Knowledge Acquisition. AAMAS 2018: 712-720 - [c27]David Isele, Eric Eaton, Mark Roberts, David W. Aha:
Modeling Consecutive Task Learning with Task Graph Agendas. AAMAS 2018: 1965-1967 - [c26]Jorge A. Mendez, Shashank Shivkumar, Eric Eaton:
Lifelong Inverse Reinforcement Learning. NeurIPS 2018: 4507-4518 - 2017
- [j18]Eric Eaton:
Teaching Integrated AI through Interdisciplinary Project-Driven Courses. AI Mag. 38(2): 13-21 (2017) - [j17]Amy McGovern, Eric Eaton:
AI profiles: an interview with Jim Kurose. AI Matters 3(1): 14-16 (2017) - [j16]Eric Eaton, Amy McGovern:
Welcome to AI matters, volume 3, issue 2. AI Matters 3(2): 3 (2017) - [j15]Amy McGovern, Eric Eaton:
AI profiles: an interview with Peter Stone. AI Matters 3(2): 8-10 (2017) - [j14]Eric Eaton, Amy McGovern:
Welcome to AI matters, volume 3, issue 3. AI Matters 3(3): 3 (2017) - [j13]Sven Koenig, Sanmay Das, Rosemary D. Paradis, Eric Eaton, Yolanda Gil, Katherine Guo, Bojun Huang, Albert Jiang, Benjamin Kuipers, Nicholas Mattei, Amy McGovern, Larry R. Medsker, Todd W. Neller, Plamen Petrov, Michael Rovatsos, David G. Stork:
ACM SIGAI activity report. AI Matters 3(3): 7-11 (2017) - [j12]Amy McGovern, Eric Eaton:
AI profiles: an interview with Maja Matarić. AI Matters 3(3): 12-14 (2017) - [j11]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. Pattern Recognit. 69: 325-335 (2017) - [c25]Christopher Clingerman, Eric Eaton:
Lifelong Learning with Gaussian Processes. ECML/PKDD (2) 2017: 690-704 - [i10]Eric Eaton, Sven Koenig, Claudia Schulz, Francesco Maurelli, John Lee, Joshua Eckroth, Mark Crowley, Richard G. Freedman, Rogelio Enrique Cardona-Rivera, Tiago Machado, Tom Williams:
Blue Sky Ideas in Artificial Intelligence Education from the EAAI 2017 New and Future AI Educator Program. CoRR abs/1702.00137 (2017) - [i9]Mohammad Rostami, Soheil Kolouri, Kyungnam Kim, Eric Eaton:
Multi-Agent Distributed Lifelong Learning for Collective Knowledge Acquisition. CoRR abs/1709.05412 (2017) - [i8]David Isele, Mohammad Rostami, Eric Eaton:
Using Task Descriptions in Lifelong Machine Learning for Improved Performance and Zero-Shot Transfer. CoRR abs/1710.03850 (2017) - [i7]José-Marcio Luna, Eric Eaton, Lyle H. Ungar, Eric S. Diffenderfer, Shane T. Jensen, Efstathios D. Gennatas, Mateo Wirth, Charles B. Simone II, Timothy D. Solberg, Gilmer Valdes:
Tree-Structured Boosting: Connections Between Gradient Boosted Stumps and Full Decision Trees. CoRR abs/1711.06793 (2017) - 2016
- [j10]Eric Eaton, Amy McGovern:
Welcome to AI Matters, volume 2, issue 3. AI Matters 2(3): 3 (2016) - [j9]Amy McGovern, Eric Eaton:
AI profiles: an interview with Peter Norvig. AI Matters 2(4): 4-6 (2016) - [c24]David Isele, Mohammad Rostami, Eric Eaton:
Using Task Features for Zero-Shot Knowledge Transfer in Lifelong Learning. IJCAI 2016: 1620-1626 - [c23]David Isele, José-Marcio Luna, Eric Eaton, Gabriel Victor de la Cruz, James Irwin, Brandon Kallaher, Matthew E. Taylor:
Lifelong learning for disturbance rejection on mobile robots. IROS 2016: 3993-3998 - [i6]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. CoRR abs/1604.05865 (2016) - [i5]Decebal Constantin Mocanu, Maria Torres Vega, Eric Eaton, Peter Stone, Antonio Liotta:
Online Contrastive Divergence with Generative Replay: Experience Replay without Storing Data. CoRR abs/1610.05555 (2016) - 2015
- [j8]Eric Eaton, Kiri L. Wagstaff:
Welcome to AI Matter: volume 2, issue 1. AI Matters 2(1): 3 (2015) - [j7]Eric Eaton, Amy McGovern:
Welcome to AI Matters, volume 2, issue 2. AI Matters 2(2): 3 (2015) - [j6]Eric Eaton, Tom Dietterich, Maria L. Gini, Barbara J. Grosz, Charles L. Isbell Jr., Subbarao Kambhampati, Michael L. Littman, Francesca Rossi, Stuart Russell, Peter Stone, Toby Walsh, Michael J. Wooldridge:
Who speaks for AI? AI Matters 2(2): 4-14 (2015) - [c22]Haitham Bou-Ammar, Eric Eaton, Paul Ruvolo, Matthew E. Taylor:
Unsupervised Cross-Domain Transfer in Policy Gradient Reinforcement Learning via Manifold Alignment. AAAI 2015: 2504-2510 - [c21]Haitham Bou-Ammar, Rasul Tutunov, Eric Eaton:
Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret. ICML 2015: 2361-2369 - [c20]Haitham Bou-Ammar, Eric Eaton, José-Marcio Luna, Paul Ruvolo:
Autonomous Cross-Domain Knowledge Transfer in Lifelong Policy Gradient Reinforcement Learning. IJCAI 2015: 3345-3351 - [e1]Matteo Leonetti, Eric Eaton:
Knowledge, Skill, and Behavior Transfer in Autonomous Robots, Papers from the 2015 AAAI Workshop, Austin, Texas, USA, January 25, 2015. AAAI Technical Report WS-15-09, AAAI Press 2015, ISBN 978-1-57735-720-9 [contents] - [i4]Stefano V. Albrecht, J. Christopher Beck, David L. Buckeridge, Adi Botea, Cornelia Caragea, Chi-Hung Chi, Theodoros Damoulas, Bistra Dilkina, Eric Eaton, Pooyan Fazli, Sam Ganzfried, Marius Lindauer, Marlos C. Machado, Yuri Malitsky, Gary Marcus, Sebastiaan A. Meijer, Francesca Rossi, Arash Shaban-Nejad, Sylvie Thiébaux, Manuela M. Veloso, Toby Walsh, Can Wang, Jie Zhang, Yu Zheng:
Reports from the 2015 AAAI Workshop Program. AI Mag. 36(2): 90-101 (2015) - [i3]Haitham Bou-Ammar, Rasul Tutunov, Eric Eaton:
Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret. CoRR abs/1505.05798 (2015) - 2014
- [j5]Eric Eaton, Carla P. Gomes, Brian Charles Williams:
Computational Sustainability. AI Mag. 35(2): 3-7 (2014) - [j4]Eric Eaton, Carla P. Gomes, Brian Charles Williams:
Computational Sustainability: Editorial Introduction to the Summer and Fall Issues. AI Mag. 35(3): 3-7 (2014) - [j3]Eric Eaton, Marie desJardins, Sara Jacob:
Multi-view constrained clustering with an incomplete mapping between views. Knowl. Inf. Syst. 38(1): 231-257 (2014) - [c19]Paul Ruvolo, Eric Eaton:
Online Multi-Task Learning via Sparse Dictionary Optimization. AAAI 2014: 2062-2068 - [c18]Vishnu Purushothaman Sreenivasan, Haitham Bou-Ammar, Eric Eaton:
Online Multi-Task Gradient Temporal-Difference Learning. AAAI 2014: 3136-3137 - [c17]Haitham Bou-Ammar, Eric Eaton, Paul Ruvolo, Matthew E. Taylor:
Online Multi-Task Learning for Policy Gradient Methods. ICML 2014: 1206-1214 - [i2]Rasul Tutunov, Haitham Bou-Ammar, Ali Jadbabaie, Eric Eaton:
On the Degree Distribution of Pólya Urn Graph Processes. CoRR abs/1410.8515 (2014) - 2013
- [j2]Vita Markman, Georgi Stojanov, Bipin Indurkhya, Takashi Kido, Keiki Takadama, George Dimitri Konidaris, Eric Eaton, Naohiro Matsumura, Renate Fruchter, Donald A. Sofge, William F. Lawless, Omid Madani, Rahul Sukthankar:
Reports of the 2013 AAAI Spring Symposium Series. AI Mag. 34(3): 93-98 (2013) - [c16]Paul Ruvolo, Eric Eaton:
Active Task Selection for Lifelong Machine Learning. AAAI 2013: 862-868 - [c15]Paul Ruvolo, Eric Eaton:
Scalable Lifelong Learning with Active Task Selection. AAAI Spring Symposium: Lifelong Machine Learning 2013 - [c14]Paul Ruvolo, Eric Eaton:
ELLA: An Efficient Lifelong Learning Algorithm. ICML (1) 2013: 507-515 - 2012
- [c13]Eric Eaton, Rachael A. Mansbach:
A Spin-Glass Model for Semi-Supervised Community Detection. AAAI 2012: 900-906 - [c12]Douglas H. Fisher, Bistra Dilkina, Eric Eaton, Carla Gomes:
Incorporating Computational Sustainability into AI Education through a Freely-Available, Collectively-Composed Supplementary Lab Text. EAAI 2012: 2369-2370 - [i1]Eric Eaton, Marie desJardins, Sara Jacob:
Multi-view constrained clustering with an incomplete mapping between views. CoRR abs/1210.2640 (2012) - 2011
- [c11]Eric Eaton, Terran Lane:
The Importance of Selective Knowledge Transfer for Lifelong Learning. Lifelong Learning 2011 - [c10]Eric Eaton, Marie desJardins:
Selective Transfer Between Learning Tasks Using Task-Based Boosting. AAAI 2011: 337-343 - 2010
- [j1]Kiri L. Wagstaff, Marie desJardins, Eric Eaton:
Modelling and learning user preferences over sets. J. Exp. Theor. Artif. Intell. 22(3): 237-268 (2010) - [c9]Eric Eaton, Gary Holness, Daniel McFarlane:
Interactive Learning Using Manifold Geometry. AAAI 2010: 437-443 - [c8]Eric Eaton, Marie desJardins, Sara Jacob:
Multi-view clustering with constraint propagation for learning with an incomplete mapping between views. CIKM 2010: 389-398
2000 – 2009
- 2009
- [c7]Eric Eaton, Gary Holness, Daniel McFarlane:
Interactive Learning Using Manifold Geometry. AAAI Fall Symposium: Manifold Learning and Its Applications 2009 - [c6]Eric Eaton, Marie desJardins:
Set-Based Boosting for Instance-Level Transfer. ICDM Workshops 2009: 422-428 - 2008
- [c5]Eric Eaton, Marie desJardins, Terran Lane:
Modeling Transfer Relationships Between Learning Tasks for Improved Inductive Transfer. ECML/PKDD (1) 2008: 317-332 - 2007
- [c4]Eric Eaton, Marie desJardins, John Stevenson:
Using Multiresolution Learning for Transfer in Image Classification. AAAI 2007: 1852-1853 - 2006
- [c3]