166 citations · 256 across the 15 of their papers we have counts for
15 papers
Tuning-Free Covariance-Aware Sequential Shrinkage for Multi-Source Estimation
Wenbo Jing, Xi Chen, Yaqi Duan +2
Modern statistical learning problems often involve multiple related data sets, in which learning efficiency on a target set can be improved by borrowing information from related so…
Binomial Smoothing for Inventory and Information Control in Supply Chains
Rene Caldentey, Avi Giloni, Clifford Hurvich +2
In many decentralized supply chains, upstream firms do not observe market demand directly and instead infer downstream conditions from the order stream. A retailer's replenishment…
Generalized Rank Regression
Jiyuan Tu, Suqi Wu, Yichen Zhang +1
Rank regression offers robustness to outliers and heavy-tailed response distributions, invariance to monotonic transformations, and improved efficiency under non-Gaussian errors, m…
Policy-Aware Design of Large-Scale Factorial Experiments
Xin Wen, Xi Chen, Will Wei Sun +1
Digital firms routinely run many online experiments on shared user populations. When product decisions are compositional, such as combinations of interface elements, flows, message…
Distributed Tensor Principal Component Analysis with Data Heterogeneity
Elynn Chen, Xi Chen, Wenbo Jing +1
As tensors become widespread in modern data analysis, Tucker low-rank Principal Component Analysis (PCA) has become essential for dimensionality reduction and structural discovery…
Online Estimation and Inference for Robust Policy Evaluation in Reinforcement Learning
Weidong Liu, Jiyuan Tu, Xi Chen +1
Reinforcement learning has emerged as one of the prominent topics attracting attention in modern statistical learning, with policy evaluation being a key component. Unlike the trad…
Acceleration of stochastic gradient descent with momentum by averaging: finite-sample rates and asymptotic normality
Kejie Tang, Weidong Liu, Yichen Zhang +1
Stochastic gradient descent with momentum (SGDM) has been widely used in many machine learning and statistical applications. Despite the observed empirical benefits of SGDM over tr…
Online Statistical Inference for Contextual Bandits via Stochastic Gradient Descent
Xiangyu Chang, Xi Chen, Zehua Lai +3
With the fast development of big data, learning the optimal decision rule by recursively updating it and making online decisions has been easier than before. We study the online st…
Online Statistical Inference in Decision-Making with Matrix Context
Qiyu Han, Will Wei Sun, Yichen Zhang
The study of online decision-making problems that leverage contextual information has drawn notable attention due to their significant applications in fields ranging from healthcar…
Distributed Estimation and Inference for Semi-parametric Binary Response Models
Xi Chen, Wenbo Jing, Weidong Liu +1
The development of modern technology has enabled data collection of unprecedented size, which poses new challenges to many statistical estimation and inference problems. This paper…
Distributed Optimization with Inexact Oracle
Kui Zhu, Yichen Zhang, Yutao Tang
In this paper, we study the distributed optimization problem using approximate first-order information. We suppose the agent can repeatedly call an inexact first-order oracle of ea…
Online Statistical Inference for Stochastic Optimization via Kiefer-Wolfowitz Methods
Xi Chen, Zehua Lai, He Li +1
This paper investigates the problem of online statistical inference of model parameters in stochastic optimization problems via the Kiefer-Wolfowitz algorithm with random search di…
First-order Newton-type Estimator for Distributed Estimation and Inference
Xi Chen, Weidong Liu, Yichen Zhang
This paper studies distributed estimation and inference for a general statistical problem with a convex loss that could be non-differentiable. For the purpose of efficient computat…
Quantile Regression Under Memory Constraint
Xi Chen, Weidong Liu, Yichen Zhang
This paper studies the inference problem in quantile regression (QR) for a large sample size but under a limited memory constraint, where the memory can only store a small batc…
Statistical Inference for Model Parameters in Stochastic Gradient Descent
Xi Chen, Jason D. Lee, Xin T. Tong +1
The stochastic gradient descent (SGD) algorithm has been widely used in statistical estimation for large-scale data due to its computational and memory efficiency. While most exist…