3 papers
cs.LG2026
"Deep lifts": Neural networks can generalize even with the dumbest optimizer
Sunil Madhow, Yu-Xiang Wang
Given a labeled dataset , consider drawing a parameter vector uniformly at random from those that interpolate the data within a width- depth-$D…
cs.LG2026
Learnable Chernoff Baselines for Inference-Time Alignment
Sunil Madhow, Yuchen Liang, Ness Shroff +2
We study inference-time reward-guided alignment for generative models. Existing methods often rely on either architecture-specific adaptations or computationally costly inference p…
cs.LG2023
Offline Policy Evaluation for Reinforcement Learning with Adaptively Collected Data
Sunil Madhow, Dan Qiao, Ming Yin +1
Developing theoretical guarantees on the sample complexity of offline RL methods is an important step towards making data-hungry RL algorithms practically viable. Currently, most r…