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ORCID2nd CoLLAs 2023: Montréal, Québec, Canada
- Sarath Chandar, Razvan Pascanu, Hanie Sedghi, Doina Precup:
Conference on Lifelong Learning Agents, 22-25 August 2023, McGill University, Montréal, Québec, Canada. Proceedings of Machine Learning Research 232, PMLR 2023 - Yingjun Du, Jiayi Shen, Xiantong Zhen, Cees G. M. Snoek:
EMO: Episodic Memory Optimization for Few-Shot Meta-Learning. 1-20 - Ali Rahimi-Kalahroudi, Janarthanan Rajendran, Ida Momennejad, Harm van Seijen, Sarath Chandar:
Replay Buffer with Local Forgetting for Adapting to Local Environment Changes in Deep Model-Based Reinforcement Learning. 21-42 - Timothée Lesort, Oleksiy Ostapenko, Pau Rodríguez, Diganta Misra, Md Rifat Arefin, Laurent Charlin, Irina Rish:
Challenging Common Assumptions about Catastrophic Forgetting and Knowledge Accumulation. 43-65 - Kushagra Chandak, Bingshan Hu, Nidhi Hegde:
Differentially Private Algorithms for Efficient Online Matroid Optimization. 66-88 - Massimo Caccia, Jonas Mueller, Taesup Kim, Laurent Charlin, Rasool Fakoor:
Task-Agnostic Continual Reinforcement Learning: Gaining Insights and Overcoming Challenges. 89-119 - Yuqing Du, Ksenia Konyushkova, Misha Denil, Akhil Raju, Jessica Landon, Felix Hill, Nando de Freitas, Serkan Cabi:
Vision-Language Models as Success Detectors. 120-136 - Eleni Nisioti, Elías Masquil, Gautier Hamon, Clément Moulin-Frier:
Autotelic Reinforcement Learning in Multi-Agent Environments. 137-161 - Randolph Linderman, Jingyang Zhang, Nathan Inkawhich, Hai Helen Li, Yiran Chen:
Fine-grain Inference on Out-of-Distribution Data with Hierarchical Classification. 162-183 - Samuel Kessler, Mateusz Ostaszewski, Michal Pawel Bortkiewicz, Mateusz Zarski, Maciej Wolczyk, Jack Parker-Holder, Stephen J. Roberts, Piotr Milos:
The Effectiveness of World Models for Continual Reinforcement Learning. 184-204 - Cédric Colas, Laetitia Teodorescu, Pierre-Yves Oudeyer, Xingdi Yuan, Marc-Alexandre Côté:
Augmenting Autotelic Agents with Large Language Models. 205-226 - Renhao Huang, Anthony Tompkins, Maurice Pagnucco, Yang Song:
Towards Single Source Domain Generalisation in Trajectory Prediction: A Motion Prior based Approach. 227-243 - Subhankar Roy, Riccardo Volpi, Gabriela Csurka, Diane Larlus:
RaSP: Relation-aware Semantic Prior for Weakly Supervised Incremental Segmentation. 244-269 - Ishita Mediratta, Minqi Jiang, Jack Parker-Holder, Michael Dennis, Eugene Vinitsky, Tim Rocktäschel:
Stabilizing Unsupervised Environment Design with a Learned Adversary. 270-291 - Shenao Zhang, Li Shen, Lei Han, Li Shen:
Learning Meta Representations for Agents in Multi-Agent Reinforcement Learning. 292-317 - Hamed Hemati, Vincenzo Lomonaco, Davide Bacciu, Damian Borth:
Partial Hypernetworks for Continual Learning. 318-336 - Sashank Pisupati, Isabel M. Berwian, Jamie Chiu, Yongjing Ren, Yael Niv:
Human inductive biases for aversive continual learning - a hierarchical Bayesian nonparametric model. 337-346 - 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. 347-357 - Amber Li, Tom Silver:
Embodied Active Learning of Relational State Abstractions for Bilevel Planning. 358-375 - Hadi Nekoei, Akilesh Badrinaaraayanan, Amit Sinha, Mohammad Amini, Janarthanan Rajendran, Aditya Mahajan, Sarath Chandar:
Dealing With Non-stationarity in Decentralized Cooperative Multi-Agent Deep Reinforcement Learning via Multi-Timescale Learning. 376-398 - Grégoire Petit, Adrian Popescu, Eden Belouadah, David Picard, Bertrand Delezoide:
PlaStIL: Plastic and Stable Exemplar-Free Class-Incremental Learning. 399-414 - Yongxin Yang, Timothy M. Hospedales:
Partial Index Tracking: A Meta-Learning Approach. 415-436 - Hamed Hemati, Andrea Cossu, Antonio Carta, Julio Hurtado, Lorenzo Pellegrini, Davide Bacciu, Vincenzo Lomonaco, Damian Borth:
Class-Incremental Learning with Repetition. 437-455 - Shubham Malaviya, Manish Shukla, Sachin Lodha:
Reducing Communication Overhead in Federated Learning for Pre-trained Language Models Using Parameter-Efficient Finetuning. 456-469 - Vincent Létourneau, Colin Bellinger, Isaac Tamblyn, Maia Fraser:
Time and temporal abstraction in continual learning: tradeoffs, analogies and regret in an active measuring setting. 470-480 - Hongyu Wang, Eibe Frank, Bernhard Pfahringer, Geoffrey Holmes:
Self-trained Centroid Classifiers for Semi-supervised Cross-domain Few-shot Learning. 481-492 - Sam Powers, Abhinav Gupta, Chris Paxton:
Evaluating Continual Learning on a Home Robot. 493-512 - Haoran Li, Jingfeng Wu, Vladimir Braverman:
Fixed Design Analysis of Regularization-Based Continual Learning. 513-533 - Alexandre Galashov, Jovana Mitrovic, Dhruva Tirumala, Yee Whye Teh, Timothy Nguyen, Arslan Chaudhry, Razvan Pascanu:
Continually learning representations at scale. 534-547 - Safa Alver, Doina Precup:
Minimal Value-Equivalent Partial Models for Scalable and Robust Planning in Lifelong Reinforcement Learning. 548-567 - Lorenzo Steccanella, Simone Totaro, Anders Jonsson:
Hierarchical Representation Learning for Markov Decision Processes. 568-585 - Boje Deforce, Bart Baesens, Jan Diels, Estefanía Serral Asensio:
MultiMix TFT: A Multi-task Mixed-Frequency Framework with Temporal Fusion Transformers. 586-600 - Gabriele Merlin, Vedant Nanda, Ruchit Rawal, Mariya Toneva:
What Happens During Finetuning of Vision Transformers: An Invariance Based Investigation. 601-619 - Zaheer Abbas, Rosie Zhao, Joseph Modayil, Adam White, Marlos C. Machado:
Loss of Plasticity in Continual Deep Reinforcement Learning. 620-636 - Sarthak Bhagat, Simon Stepputtis, Joseph Campbell, Katia P. Sycara:
Sample-Efficient Learning of Novel Visual Concepts. 637-657 - Tom Dupuis, Jaonary Rabarisoa, Quoc-Cuong Pham, David Filliat:
VIBR: Learning View-Invariant Value Functions for Robust Visual Control. 658-682 - Hsing-Huan Chung, Joydeep Ghosh:
Incremental Unsupervised Domain Adaptation on Evolving Graphs. 683-702 - Banafsheh Rafiee, Sina Ghiassian, Jun Jin, Richard S. Sutton, Jun Luo, Adam White:
Auxiliary task discovery through generate-and-test. 703-714 - Réda Alami, Mohammed Mahfoud, Eric Moulines:
Restarted Bayesian Online Change-point Detection for Non-Stationary Markov Decision Processes. 715-744 - Kristopher De Asis, Eric Graves, Richard S. Sutton:
Value-aware Importance Weighting for Off-policy Reinforcement Learning. 745-763 - Gwen Legate, Lucas Caccia, Eugene Belilovsky:
Re-Weighted Softmax Cross-Entropy to Control Forgetting in Federated Learning. 764-780 - Vincent Liu, Han Wang, Ruo Yu Tao, Khurram Javed, Adam White, Martha White:
Measuring and Mitigating Interference in Reinforcement Learning. 781-795 - Lior Friedman, Ron Meir:
Adaptive Meta-Learning via data-dependent PAC-Bayes bounds. 796-810 - Christopher McClurg, Ali Ayub, Harsh Tyagi, Sarah Michele Rajtmajer, Alan R. Wagner:
Active Class Selection for Few-Shot Class-Incremental Learning. 811-827 - Albin Soutif-Cormerais, Antonio Carta, Joost van de Weijer:
Improving Online Continual Learning Performance and Stability with Temporal Ensembles. 828-845 - Zifan Xu, Yulin Zhang, Shahaf S. Shperberg, Reuth Mirsky, Yuqian Jiang, Bo Liu, Peter Stone:
Model-Based Meta Automatic Curriculum Learning. 846-860 - Hadi Nekoei, Xutong Zhao, Janarthanan Rajendran, Miao Liu, Sarath Chandar:
Towards Few-shot Coordination: Revisiting Ad-hoc Teamplay Challenge In the Game of Hanabi. 861-877 - Massimiliano Patacchiola, Mingfei Sun, Katja Hofmann, Richard E. Turner:
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation. 878-908 - Muberra Ozmen, Joseph Cotnareanu, Mark Coates:
Substituting Data Annotation with Balanced Neighbourhoods and Collective Loss in Multi-label Text Classification. 909-922 - Tejas Srinivasan, Furong Jia, Mohammad Rostami, Jesse Thomason:
I2I: Initializing Adapters with Improvised Knowledge. 923-935 - Saptarshi Nath, Christos Peridis, Eseoghene Ben-Iwhiwhu, Xinran Liu, Shirin Dora, Cong Liu, Soheil Kolouri, Andrea Soltoggio:
Sharing Lifelong Reinforcement Learning Knowledge via Modulating Masks. 936-960 - Thang Doan, Seyed-Iman Mirzadeh, Mehrdad Farajtabar:
Continual Learning Beyond a Single Model. 961-991 - Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash:
Improving Performance in Continual Learning Tasks using Bio-Inspired Architectures. 992-1008 - Arip Asadulaev, Vitaly Shutov, Alexander Korotin, Alexander Panfilov, Vladislava Kontsevaya, Andrey Filchenkov:
A Minimalist Approach for Domain Adaptation with Optimal Transport. 1009-1024 - Peter G. Chang, Gerardo Duràn-Martín, Alexander Y. Shestopaloff, Matt Jones, Kevin Patrick Murphy:
Low-rank extended Kalman filtering for online learning of neural networks from streaming data. 1025-1071 - Joseph Campbell, Yue Guo, Fiona Xie, Simon Stepputtis, Katia P. Sycara:
Introspective Action Advising for Interpretable Transfer Learning. 1072-1090 - Han Sun, Rui Gong, Konrad Schindler, Luc Van Gool:
SF-FSDA: Source-Free Few-Shot Domain Adaptive Object Detection with Efficient Labeled Data Factory. 1091-1111
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last updated on 2024-09-26 00:45 CEST by the dblp team
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