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cs.LG2024
Transcendence: Generative Models Can Outperform The Experts That Train Them
Edwin Zhang, Vincent Zhu, Naomi Saphra +5
Generative models are trained with the simple objective of imitating the conditional probability distribution induced by the data they are trained on. Therefore, when trained on da…
cs.LG2023
Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping
Lauren H. Cooke, Harvey Klyne, Edwin Zhang +3
Inverse reinforcement learning (IRL) is computationally challenging, with common approaches requiring the solution of multiple reinforcement learning (RL) sub-problems. This work m…
cs.LG2023
Towards a Pretrained Model for Restless Bandits via Multi-arm Generalization
Yunfan Zhao, Nikhil Behari, Edward Hughes +5
Restless multi-arm bandits (RMABs), a class of resource allocation problems with broad application in areas such as healthcare, online advertising, and anti-poaching, have recently…