6 papers · 1 filter
Coupled Calibration and Learning: Mitigating Teacher Bias in LLM Distillation without Target-Domain Reward Feedback
Haichen Hu, Yuheng Zhang, David Simchi-Levi
Large language model (LLM) distillation aims to transfer the capabilities of a powerful teacher to a smaller student. Direct imitation, however, can also transfer the teacher's sys…
Sharp Structure-Agnostic Minimax Risk for Partial Linear Models
Haichen Hu, David Simchi-Levi
We characterize the sharp structure-agnostic minimax risk for coefficient estimation in the partial linear model when the outcome and treatment nuisances are learned by two distinc…
Simultaneous Pointwise Majorization for Mixed Tail Processes with Applications in Gaussian Chaos and Ergodic Diffusions
Haichen Hu, David Simchi-Levi
Classical chaining controls an indexed stochastic process through a single worst-case bound and can therefore obscure substantial variation across the index set. We develop the fir…
Optimizing the Preconditioner: A Black-box Online-to-Nonconvex Conversion with Static Regret Minimization Oracles
Haichen Hu, David Simchi-Levi
Stochastic nonconvex optimization is central to training deep networks and LLMs in modern machine learning. We give a black-box reduction from stochastic nonconvex optimization to…
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…