10 papers
An Isotropy-Preserving Spectral Cap for Muon: Theory and Three Case Studies
Jiachun Li
Muon and related matrix-sign optimizers are increasingly used to pre-train large language models, but their effect on the internal geometry of individual weight matrices is not wel…
Semiparametric Efficiency in Sequential Experiments: Characterization and Design via Average Propensity
Jiachun Li, David Simchi-Levi
Modern experiments, including evaluations of AI-enabled services and platform interventions, often depart from independent and identically distributed (i.i.d.) sampling because ass…
Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
Jiachun Li, David Simchi-Levi
Adaptive experiments for average treatment effects (ATE) require randomized allocations balancing valid inference with statistical efficiency. The oracle design is a covariate-depe…
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…
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…
Beyond ATE: Multi-Criteria Design for A/B Testing
Jiachun Li, Kaining Shi, David Simchi-Levi
In the era of large-scale AI deployment and high-stakes clinical trials, adaptive experimentation faces a ``trilemma'' of conflicting objectives: minimizing cumulative regret (welf…
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…
Optimal Adaptive Experimental Design for Estimating Treatment Effect
Jiachun Li, David Simchi-Levi, Yunxiao Zhao
Given n experiment subjects with potentially heterogeneous covariates and two possible treatments, namely active treatment and control, this paper addresses the fundamental questio…
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…
Privacy Preserving Adaptive Experiment Design
Jiachun Li, Kaining Shi, David Simchi-Levi
Adaptive experiment is widely adopted to estimate conditional average treatment effect (CATE) in clinical trials and many other scenarios. While the primary goal in experiment is t…