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Conference and Workshop Papers
- 2021
[c3]Jad Rahme, Samy Jelassi, Joan Bruna, S. Matthew Weinberg:
A Permutation-Equivariant Neural Network Architecture For Auction Design. AAAI 2021: 5664-5672- [c2]Jad Rahme, Samy Jelassi, S. Matthew Weinberg
:
Auction Learning as a Two-Player Game. ICLR 2021 - [c1]Dibya Ghosh, Jad Rahme, Aviral Kumar, Amy Zhang, Ryan P. Adams, Sergey Levine:
Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability. NeurIPS 2021: 25502-25515
Informal and Other Publications
- 2022
- [i5]Jad Rahme:
Learning Algorithms for Intelligent Agents and Mechanisms. CoRR abs/2210.02654 (2022) - 2021
- [i4]Dibya Ghosh, Jad Rahme, Aviral Kumar, Amy Zhang, Ryan P. Adams, Sergey Levine:
Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability. CoRR abs/2107.06277 (2021) - 2020
- [i3]Jad Rahme, Samy Jelassi, Joan Bruna, S. Matthew Weinberg:
A Permutation-Equivariant Neural Network Architecture For Auction Design. CoRR abs/2003.01497 (2020) - [i2]Jad Rahme, Samy Jelassi, S. Matthew Weinberg:
Auction learning as a two-player game. CoRR abs/2006.05684 (2020) - 2019
- [i1]Jad Rahme, Ryan P. Adams:
A Theoretical Connection Between Statistical Physics and Reinforcement Learning. CoRR abs/1906.10228 (2019)
Coauthor Index
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