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A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems


Jan 18, 2023
Megan M. Baker, Alexander New, Mario Aguilar-Simon, Ziad Al-Halah, Sébastien M. R. Arnold, Ese Ben-Iwhiwhu, Andrew P. Brna, Ethan Brooks, Ryan C. Brown, Zachary Daniels, Anurag 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 Learned-Miller, Seungwon Lee, Michael L. 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

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* To appear in Neural Networks 

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Land Use Prediction using Electro-Optical to SAR Few-Shot Transfer Learning


Dec 04, 2022
Marcel Hussing, Karen Li, Eric Eaton

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* Published at Tackling Climate Change with Machine Learning workshop at NeurIPS 2022 

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How to Reuse and Compose Knowledge for a Lifetime of Tasks: A Survey on Continual Learning and Functional Composition


Jul 15, 2022
Jorge A. Mendez, Eric Eaton

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CompoSuite: A Compositional Reinforcement Learning Benchmark


Jul 08, 2022
Jorge A. Mendez, Marcel Hussing, Meghna Gummadi, Eric Eaton

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* Published at 1st Conference on Lifelong Learning Agents, 2022; code: https://github.com/Lifelong-ML/CompoSuite 

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Lifelong Inverse Reinforcement Learning


Jul 01, 2022
Jorge A. Mendez, Shashank Shivkumar, Eric Eaton

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* Published in NeurIPS 2018. Code: https://github.com/Lifelong-ML/ELIRL 

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Modular Lifelong Reinforcement Learning via Neural Composition


Jul 01, 2022
Jorge A. Mendez, Harm van Seijen, Eric Eaton

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* Published at ICLR 2022. Code: https://github.com/Lifelong-ML/Mendez2022ModularLifelongRL 

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SHELS: Exclusive Feature Sets for Novelty Detection and Continual Learning Without Class Boundaries


Jun 28, 2022
Meghna Gummadi, David Kent, Jorge A. Mendez, Eric Eaton

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Gap Minimization for Knowledge Sharing and Transfer


Jan 26, 2022
Boyu Wang, Jorge Mendez, Changjian Shui, Fan Zhou, Di Wu, Christian Gagné, Eric Eaton

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Prospective Learning: Back to the Future


Jan 19, 2022
Joshua T. Vogelstein, Timothy Verstynen, Konrad P. Kording, Leyla Isik, John W. Krakauer, Ralph Etienne-Cummings, Elizabeth L. Ogburn, Carey E. Priebe, Randal Burns, Kwame Kutten, James J. Knierim, James B. Potash, Thomas Hartung, Lena Smirnova, Paul Worley, Alena Savonenko, Ian Phillips, Michael I. Miller, Rene Vidal, Jeremias Sulam, Adam Charles, Noah J. Cowan, Maxim Bichuch, Archana Venkataraman, Chen Li, Nitish Thakor, Justus M Kebschull, Marilyn Albert, Jinchong Xu, Marshall Hussain Shuler, Brian Caffo, 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 Ungar, Leila Wehbe, Adam Kepecs, Amy Christensen, Onyema Osuagwu, Bing Brunton, Brett Mensh, Alysson R. Muotri, Gabriel Silva, Francesca Puppo, Florian Engert, Elizabeth Hillman, Julia Brown, Chris White, Weiwei Yang

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Towards a theory of out-of-distribution learning


Oct 07, 2021
Ali Geisa, Ronak Mehta, Hayden S. Helm, Jayanta Dey, Eric Eaton, Jeffery Dick, Carey E. Priebe, Joshua T. Vogelstein

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