1 citations · 2 across the 25 of their papers we have counts for
29 papers
Pessimistic Minimax Learning for Public-Private Information Games under Unilateral Coverage
Shuze Daniel Liu, Claire Chen, Jiuqi Wang +1
We study offline learning in two-player zero-sum contextual games with public and private information, motivated by strategic settings such as auctions and negotiations with privat…
Minimax-Optimal Online Contract Design with Unrestricted Bounded Contracts
Rui Ai, David Simchi-Levi, Han Zhong
We study repeated contract design when a principal observes outcomes but not the actions that generate them. The principal may use any bounded outcome-contingent payment vector, an…
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…
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…
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…
OR-Transformer: Scaling Real-Time Decision-Making to 1,000 Items
Shuze Daniel Liu, David Simchi-Levi, Claire Chen +2
Modern supply chain operations can require coordinating replenishment across thousands of heterogeneous items under correlated stochastic demand, heterogeneous lead times, and shar…
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…
When Reasoning Narrows the Move: Diversity Collapse in LLM Game Play
Junyi Sha, Renfei Tan, David Simchi-Levi
Supervised fine-tuning (SFT) is widely used to adapt large language models to downstream tasks, but its effect on behavioral diversity in sequential decision-making remains under-e…
Strategic Bargaining in Multi-Buyer Markets: Reinforcement Learning from Verifiable Rewards for LLM Negotiations
Shuze Daniel Liu, Claire Chen, Jiabao Sean Xiao +2
Negotiation is a fundamental strategic interaction in management science, characterized by agents attempting to reach agreements while protecting private information, such as reser…
Low Rank for Rank: Uncertainty-Aware Task-Specific LLM Ranking under Sparse Pairwise Comparisons
Jiachun Li, David Simchi-Levi, Will Wei Sun
Pairwise human-preference platforms such as Chatbot Arena have become central to large language model (LLM) evaluation, yet reliable task-specific ranking remains challenging. Glob…
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…
Instructing LLMs to Negotiate using Reinforcement Learning with Verifiable Rewards
Shuze Daniel Liu, Claire Chen, Jiabao Sean Xiao +4
The recent advancement of Large Language Models (LLMs) has established their potential as autonomous interactive agents. However, they often struggle in strategic games of incomple…
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…
LLM Evaluation as Tensor Completion: Low Rank Structure and Semiparametric Efficiency
Jiachun Li, David Simchi-Levi, Will Wei Sun
Large language model (LLM) evaluation platforms increasingly rely on pairwise human judgments. These data are noisy, sparse, and non-uniform, yet leaderboards are reported with lim…
ShapE-GRPO: Shapley-Enhanced Reward Allocation for Multi-Candidate LLM Training
Rui Ai, Yu Pan, David Simchi-Levi +1
In user-agent interaction scenarios such as recommendation, brainstorming, and code suggestion, Large Language Models (LLMs) often generate sets of candidate recommendations where…
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…
GenAI vs. Human Creators: Procurement Mechanism Design in Two-/Three-Layer Markets
Rui Ai, David Simchi-Levi, Haifeng Xu
With the rapid advancement of generative AI (GenAI), mechanism design adapted to its unique characteristics poses new theoretical and practical challenges. Unlike traditional goods…
Beyond Majority Voting: LLM Aggregation by Leveraging Higher-Order Information
Rui Ai, Yuqi Pan, David Simchi-Levi +2
With the rapid progress of multi-agent large language model (LLM) reasoning, how to effectively aggregate answers from multiple LLMs has emerged as a fundamental challenge. Standar…
Solve Smart, Not Often: Policy Learning for Costly MILP Re-solving
Rui Ai, Hugo De Oliveira Barbalho, Sirui Li +3
A common challenge in real-time operations is deciding whether to re-solve an optimization problem or continue using an existing solution. While modern data platforms may collect i…
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…
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…
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…
Beyond Covariance Matrix: The Statistical Complexity of Private Linear Regression
Fan Chen, Jiachun Li, Alexander Rakhlin +1
We study the statistical complexity of private linear regression under an unknown, potentially ill-conditioned covariate distribution. Somewhat surprisingly, under privacy constrai…