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cs.LG2025
Accelerated Online Reinforcement Learning using Auxiliary Start State Distributions
Aman Mehra, Alexandre Capone, Jeff Schneider
A long-standing problem in online reinforcement learning (RL) is of ensuring sample efficiency, which stems from an inability to explore environments efficiently. Most attempts at…
cs.LG2024★ 1 cited
Predicting the Performance of Foundation Models via Agreement-on-the-Line
Rahul Saxena, Taeyoun Kim, Aman Mehra +3
Estimating the out-of-distribution performance in regimes where labels are scarce is critical to safely deploy foundation models. Recently, it was shown that ensembles of neural ne…