10 citations · 21 across the 5 of their papers we have counts for
5 papers
Learning Algorithms for Intelligent Agents and Mechanisms
Jad Rahme
In this thesis, we research learning algorithms for optimal decision making in two different contexts, Reinforcement Learning in Part I and Auction Design in Part II. Reinforcement…
Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability
Dibya Ghosh, Jad Rahme, Aviral Kumar +3
Generalization is a central challenge for the deployment of reinforcement learning (RL) systems in the real world. In this paper, we show that the sequential structure of the RL pr…
Auction learning as a two-player game
Jad Rahme, Samy Jelassi, S. Matthew Weinberg
Designing an incentive compatible auction that maximizes expected revenue is a central problem in Auction Design. While theoretical approaches to the problem have hit some limits,…
A Permutation-Equivariant Neural Network Architecture For Auction Design
Jad Rahme, Samy Jelassi, Joan Bruna +1
Designing an incentive compatible auction that maximizes expected revenue is a central problem in Auction Design. Theoretical approaches to the problem have hit some limits in the…
A Theoretical Connection Between Statistical Physics and Reinforcement Learning
Jad Rahme, Ryan P. Adams
Sequential decision making in the presence of uncertainty and stochastic dynamics gives rise to distributions over state/action trajectories in reinforcement learning (RL) and opti…