1 citations · 2 across the 6 of their papers we have counts for
6 papers
SGD with Dependent Data: Optimal Estimation, Regret, and Inference
Yinan Shen, Yichen Zhang, Wen-Xin Zhou
This work investigates the performance of the final iterate produced by stochastic gradient descent (SGD) under temporally dependent data. We consider two complementary sources of…
Improved performance guarantees for Tukey's median
Stanislav Minsker, Yinan Shen
Is there a natural way to order data in dimension greater than one? The approach based on the notion of data depth, often associated with John Tukey, is among the most popular. Tuk…
Online Quantile Regression
Yinan Shen, Dong Xia, Wen-Xin Zhou
This paper addresses the challenge of integrating sequentially arriving data within the quantile regression framework, where the number of features is allowed to grow with the numb…
Quantile and pseudo-Huber Tensor Decomposition
Yinan Shen, Dong Xia
This paper studies the computational and statistical aspects of quantile and pseudo-Huber tensor decomposition. The integrated investigation of computational and statistical issues…
Computationally Efficient and Statistically Optimal Robust High-Dimensional Linear Regression
Yinan Shen, Jingyang Li, Jian-Feng Cai +1
High-dimensional linear regression under heavy-tailed noise or outlier corruption is challenging, both computationally and statistically. Convex approaches have been proven statist…
Computationally Efficient and Statistically Optimal Robust Low-rank Matrix and Tensor Estimation
Yinan Shen, Jingyang Li, Jian-Feng Cai +1
Low-rank matrix estimation under heavy-tailed noise is challenging, both computationally and statistically. Convex approaches have been proven statistically optimal but suffer from…