World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
45
Citations
16511
World Ranking
5326
National Ranking
1487

Overview

Lin Xiao is affiliated with Facebook in the United States and has contributed extensively to the fields of computer science and engineering. Their research primarily spans artificial intelligence, computational mechanics, management science and operations research, computational theory and mathematics, and numerical analysis.

Their work frequently addresses topics such as stochastic gradient optimization techniques, sparse and compressive sensing methods, optimal experimental design methods, advanced optimization algorithms, antenna design and optimization, advanced multi-objective optimization algorithms, and human pose and action recognition.

Lin Xiao has authored several research papers published across various reputable scientific venues. Recent publications include:

  • Accelerated Bregman proximal gradient methods for relatively smooth convex optimization, 2021, Computational Optimization and Applications
  • Stochastic Optimization with Decision-Dependent Distributions, 2022, Mathematics of Operations Research
  • MultiLevel Composite Stochastic Optimization via Nested Variance Reduction, 2021, SIAM Journal on Optimization
  • Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization, 2020, arXiv (Cornell University)
  • Stochastic variance-reduced prox-linear algorithms for nonconvex composite optimization, 2021, Mathematical Programming

Lin Xiao collaborates frequently with a number of researchers. Frequent co-authors include Shanqi Pang, Junyu Zhang, Filip Hanzely, Peter Richtárik, and Dmitriy Drusvyatskiy.

Their work has been published predominantly in venues such as arXiv (Cornell University), Stat, Statistics & Probability Letters, Journal of acting studies, and Computational Optimization and Applications.

Best Publications

  • Fast linear iterations for distributed averaging

    Lin Xiao;Stephen P. Boyd

  • A scheme for robust distributed sensor fusion based on average consensus

    Lin Xiao;Stephen Boyd;Sanjay Lall

  • Distributed average consensus with least-mean-square deviation

    Lin Xiao;Stephen Boyd;Seung-Jean Kim

  • Energy-aware server provisioning and load dispatching for connection-intensive internet services

    Gong Chen;Wenbo He;Jie Liu;Suman Nath

  • Fastest mixing Markov chain on a graph

    Stephen Boyd;Persi Diaconis;Lin Xiao

  • A PROXIMAL STOCHASTIC GRADIENT METHOD WITH PROGRESSIVE VARIANCE REDUCTION

    Lin Xiao;Tong Zhang;Tong Zhang

  • Simultaneous routing and resource allocation via dual decomposition

    Lin Xiao;M. Johansson;S.P. Boyd

  • Optimal distributed online prediction using mini-batches

    Ofer Dekel;Ran Gilad-Bachrach;Ohad Shamir;Lin Xiao

  • Control with random communication delays via a discrete-time jump system approach

    Lin Xiao;A. Hassibi;J.P. How

  • Optimal Scaling of a Gradient Method for Distributed Resource Allocation

    L. Xiao;S. Boyd

  • Optimal Algorithms for Online Convex Optimization with Multi-Point Bandit Feedback.

    Alekh Agarwal;Ofer Dekel;Lin Xiao

  • Cross-layer optimization of wireless networks using nonlinear column generation

    M. Johansson;L. Xiao

  • On the complexity analysis of randomized block-coordinate descent methods

    Zhaosong Lu;Lin Xiao

  • A space-time diffusion scheme for peer-to-peer least-squares estimation

    Lin Xiao;Stephen Boyd;Sanjay Lall

  • Learning to classify with missing and corrupted features

    Ofer Dekel;Ohad Shamir;Lin Xiao

  • The Fastest Mixing Markov Process on a Graph and a Connection to a Maximum Variance Unfolding Problem

    Jun Sun;Stephen Boyd;Lin Xiao;Persi Diaconis

  • SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation

    Bo Dai;Albert Shaw;Lihong Li;Lin Xiao

  • Least-Squares Covariance Matrix Adjustment

    Stephen Boyd;Lin Xiao

  • Joint optimization of communication rates and linear systems

    Lin Xiao;M. Johansson;H. Hindi;S. Boyd

  • DiSCO: Distributed Optimization for Self-Concordant Empirical Loss

    Unknown

Frequent Co-Authors

Stephen Boyd
Stephen Boyd Stanford University
Mikael Johansson
Mikael Johansson Royal Institute of Technology
Ofer Dekel
Ofer Dekel Microsoft (United States)
Li Deng
Li Deng Citadel
Jianfeng Gao
Jianfeng Gao Microsoft (United States)
Lihong Li
Lihong Li Amazon (United States)
Persi Diaconis
Persi Diaconis Stanford University
Pengchuan Zhang
Pengchuan Zhang Facebook (United States)
Ohad Shamir
Ohad Shamir Weizmann Institute of Science
Le Song
Le Song Mohamed bin Zayed University of Artificial Intelligence

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