1 citations · 2 across the 25 of their papers we have counts for
6 papers · 1 filter
Optimal Value Inference for Reinforcement Learning
Nan Lu, Ethan Lee, James M. Robins +2
We study offline inference for the optimal value in reinforcement learning under finite state and action spaces. Two new nuisances are derived as fixed points of a self-induced Bel…
Perturbing the Derivative: Wild Refitting for Model-Free Evaluation of Machine Learning Models under Bregman Losses
Haichen Hu, David Simchi-Levi
We study the excess risk evaluation of classical penalized empirical risk minimization (ERM) with Bregman losses. We show that by leveraging the idea of wild refitting, one can eff…
Eluder-Dimension-Free Regret for Stage-Wise Constrained Contextual Bandits via Offline Regression
Haichen Hu, David Simchi-Levi, Navid Azizan
Contextual bandits with stage-wise constraints model decisions that must control risk while learning from their outcomes. We study this problem when only the selected action's rewa…
Contextual Online Decision Making with Infinite-Dimensional Functional Regression
Haichen Hu, Rui Ai, Stephen Bates +1
Contextual sequential decision-making problems play a crucial role in machine learning, encompassing a wide range of downstream applications such as bandits, sequential hypothesis…
On the Optimal Regret of Locally Private Linear Contextual Bandit
Jiachun Li, David Simchi-Levi, Yining Wang
Contextual bandit with linear reward functions is among one of the most extensively studied models in bandit and online learning research. Recently, there has been increasing inter…
Utility Fairness in Contextual Dynamic Pricing with Demand Learning
Xi Chen, David Simchi-Levi, Yining Wang
This paper introduces a novel contextual bandit algorithm for personalized pricing under utility fairness constraints in scenarios with uncertain demand, achieving an optimal regre…