6 papers · 1 filter
Optimizing the Preconditioner: A Black-box Online-to-Nonconvex Conversion with Static Regret Minimization Oracles
Haichen Hu, David Simchi-Levi
We study whether stochastic nonconvex optimization can be reduced to ordinary static regret minimization in online convex optimization in a black-box manner. For smooth nonconvex o…
Model-Based Reinforcement Learning with Double Oracle Efficiency in Policy Optimization and Offline Estimation
Haichen Hu, Jian Qian, David Simchi-Levi
Reinforcement learning (RL) in large environments often suffers from severe computational bottlenecks, as conventional regret minimization algorithms require repeated, costly calls…
Interleaved Resampling and Refitting: Data and Compute-Efficient Evaluation of Black-Box Predictors
Haichen Hu, David Simchi-Levi
We study the problem of evaluating the excess risk of large-scale empirical risk minimization under the square loss. Leveraging the idea of wild refitting and resampling, we assume…
Perturbing the Derivative: Doubly Wild Refitting for Model-Free Evaluation of Opaque Machine Learning Predictors
Haichen Hu, David Simchi-Levi
We study the problem of excess risk evaluation for empirical risk minimization (ERM) under convex losses. We show that by leveraging the idea of wild refitting, one can upper bound…
Pre-Trained AI Model Assisted Online Decision-Making under Missing Covariates: A Theoretical Perspective
Haichen Hu, David Simchi-Levi
We study a sequential contextual decision-making problem in which certain covariates are missing but can be imputed using a pre-trained AI model. From a theoretical perspective, we…
Offline Oracle-Efficient Learning for Contextual MDPs via Layerwise Exploration-Exploitation Tradeoff
Jian Qian, Haichen Hu, David Simchi-Levi
Motivated by the recent discovery of a statistical and computational reduction from contextual bandits to offline regression (Simchi-Levi and Xu, 2021), we address the general (sto…