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cs.LG2026
Oracle-Efficient and Parameter-Free Agnostic Smoothed Online Learning
Sasha Voitovych, Adam Block, Alexander Rakhlin +1
Online learning is an attractive framework in many domains because it permits well-defined learning even when data are dependent or chosen adversarially. This generality, however,…
cs.LG2026
From Curiosity to Caution: Mitigating Reward Hacking for Best-of-N with Pessimism
Zhuohao Yu, Zhiwei Steven Wu, Adam Block
Inference-time compute scaling has emerged as a powerful paradigm for improving language model performance on a wide range of tasks, but the question of how best to use the additio…
stat.ML2026
Partition Function Estimation under Bounded f-Divergence
Adam Block, Abhishek Shetty
We study the statistical complexity of estimating partition functions given sample access to a proposal distribution and an unnormalized density ratio for a target distribution. Wh…