327 citations · 2.1k across the 124 of their papers we have counts for
15 papers · 1 filter
Provably Efficient High-Dimensional Bandit Learning with Batched Feedbacks
Jianqing Fan, Zhaoran Wang, Zhuoran Yang +1
We study high-dimensional multi-armed contextual bandits with batched feedback where the steps of online interactions are divided into batches. In specific, each batch coll…
Contextual Dynamic Pricing with Strategic Buyers
Pangpang Liu, Zhuoran Yang, Zhaoran Wang +1
Personalized pricing, which involves tailoring prices based on individual characteristics, is commonly used by firms to implement a consumer-specific pricing policy. In this proces…
What and How does In-Context Learning Learn? Bayesian Model Averaging, Parameterization, and Generalization
Yufeng Zhang, Fengzhuo Zhang, Zhuoran Yang +1
In this paper, we conduct a comprehensive study of In-Context Learning (ICL) by addressing several open questions: (a) What type of ICL estimator is learned by large language model…
Offline Reinforcement Learning for Human-Guided Human-Machine Interaction with Private Information
Zuyue Fu, Zhengling Qi, Zhuoran Yang +2
Motivated by the human-machine interaction such as training chatbots for improving customer satisfaction, we study human-guided human-machine interaction involving private informat…
Strategic Decision-Making in the Presence of Information Asymmetry: Provably Efficient RL with Algorithmic Instruments
Mengxin Yu, Zhuoran Yang, Jianqing Fan
We study offline reinforcement learning under a novel model called strategic MDP, which characterizes the strategic interactions between a principal and a sequence of myopic agents…
Online Bootstrap Inference For Policy Evaluation in Reinforcement Learning
Pratik Ramprasad, Yuantong Li, Zhuoran Yang +3
The recent emergence of reinforcement learning has created a demand for robust statistical inference methods for the parameter estimates computed using these algorithms. Existing m…
Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning
Luofeng Liao, Zuyue Fu, Zhuoran Yang +3
In offline reinforcement learning (RL) an optimal policy is learned solely from a priori collected observational data. However, in observational data, actions are often confounded…
Understanding Implicit Regularization in Over-Parameterized Single Index Model
Jianqing Fan, Zhuoran Yang, Mengxin Yu
In this paper, we leverage over-parameterization to design regularization-free algorithms for the high-dimensional single index model and provide theoretical guarantees for the ind…
Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approach
Luofeng Liao, You-Lin Chen, Zhuoran Yang +3
Structural equation models (SEMs) are widely used in sciences, ranging from economics to psychology, to uncover causal relationships underlying a complex system under consideration…
Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees
Sen Na, Yuwei Luo, Zhuoran Yang +2
Graph representation learning is a ubiquitous task in machine learning where the goal is to embed each vertex into a low-dimensional vector space. We consider the bipartite graph a…
Provable Gaussian Embedding with One Observation
Ming Yu, Zhuoran Yang, Tuo Zhao +2
The success of machine learning methods heavily relies on having an appropriate representation for data at hand. Traditionally, machine learning approaches relied on user-defined h…
High-dimensional Varying Index Coefficient Models via Stein's Identity
Sen Na, Zhuoran Yang, Zhaoran Wang +1
We study the parameter estimation problem for a varying index coefficient model in high dimensions. Unlike the most existing works that iteratively estimate the parameters and link…
Misspecified Nonconvex Statistical Optimization for Phase Retrieval
Zhuoran Yang, Lin F. Yang, Ethan X. Fang +3
Existing nonconvex statistical optimization theory and methods crucially rely on the correct specification of the underlying "true" statistical models. To address this issue, we ta…
Sparse Nonlinear Regression: Parameter Estimation and Asymptotic Inference
Zhuoran Yang, Zhaoran Wang, Han Liu +2
We study parameter estimation and asymptotic inference for sparse nonlinear regression. More specifically, we assume the data are given by , where is n…
On Semiparametric Exponential Family Graphical Models
Zhuoran Yang, Yang Ning, Han Liu
We propose a new class of semiparametric exponential family graphical models for the analysis of high dimensional mixed data. Different from the existing mixed graphical models, we…