1 citations · 2 across the 8 of their papers we have counts for
10 papers
The Price of Hidden Curvature: Improved Lower Bounds for Bandit Convex Optimization
Nived Rajaraman, Yanjun Han
We establish improved lower bounds on the minimax expected regret of stochastic bandit convex optimization for -Lipschitz functions on the -dimensional Euclidean ball. For ti…
Interactive Learning of Single-Index Models via Stochastic Gradient Descent
Nived Rajaraman, Yanjun Han
Stochastic gradient descent (SGD) is a cornerstone algorithm for high-dimensional optimization, renowned for its empirical successes. Recent theoretical advances have provided a de…
PETS: A Principled Framework Towards Optimal Trajectory Allocation for Efficient Test-Time Self-Consistency
Zhangyi Liu, Huaizhi Qu, Xiaowei Yin +4
Test-time scaling can improve model performance by aggregating stochastic reasoning trajectories. However, achieving sample-efficient test-time self-consistency under a limited bud…
Universal priors: solving empirical Bayes via Bayesian inference and pretraining
Nick Cannella, Anzo Teh, Yanjun Han +1
We theoretically justify the recent empirical finding of [Teh et al., 2025] that a transformer pretrained on synthetically generated data achieves strong performance on empirical B…
Sharp mean-field analysis of permutation mixtures and permutation-invariant decisions
Yiguo Liang, Yanjun Han
We develop sharp bounds on the statistical distance between high-dimensional permutation mixtures and their i.i.d. counterparts. Our approach establishes a new geometric link betwe…
Evolution of Information in Interactive Decision Making: A Case Study for Multi-Armed Bandits
Yuzhou Gu, Yanjun Han, Jian Qian
We study the evolution of information in interactive decision making through the lens of a stochastic multi-armed bandit problem. Focusing on a fundamental example where a unique o…
Assouad, Fano, and Le Cam with Interaction: A Unifying Lower Bound Framework and Characterization for Bandit Learnability
Fan Chen, Dylan J. Foster, Yanjun Han +3
We develop a unifying framework for information-theoretic lower bound in statistical estimation and interactive decision making. Classical lower bound techniques -- such as Fano's…
Causal Inference with High-dimensional Discrete Covariates
Zhenghao Zeng, Sivaraman Balakrishnan, Yanjun Han +1
When estimating causal effects from observational studies, researchers often need to adjust for many covariates to deconfound the non-causal relationship between exposure and outco…
Online Estimation via Offline Estimation: An Information-Theoretic Framework
Dylan J. Foster, Yanjun Han, Jian Qian +1
The classical theory of statistical estimation aims to estimate a parameter of interest under data generated from a fixed design ("offline estimation"), while the contemporary t…
Covariance alignment: from maximum likelihood estimation to Gromov-Wasserstein
Yanjun Han, Philippe Rigollet, George Stepaniants
Feature alignment methods are used in many scientific disciplines for data pooling, annotation, and comparison. As an instance of a permutation learning problem, feature alignment…