Lin Xiao – Papers

Google scholar page

2022

• On the convergence rates of policy gradient methods
  Lin Xiao
  arXiv preprint, 2022.

2021

• On self-concordant barriers for generalized power cones
  Scott Roy and Lin Xiao
  Optimization Letters, published online, June, 2021.

• From low probability to high confidence in stochastic convex optimization
  Damek Davis, Dmitriy Drusvyatskiy, Lin Xiao, and Junyu Zhang
  Journal of Machine Learning Research, 22(49):1-38, 2021.

• Accelerated Bregman proximal gradient methods for relatively smooth convex optimization
  Filip Hanely, Peter Richtarik, and Lin Xiao
  Computational Optimization and Applications, 79:405-440, 2021.

• MultiLevel Composite Stochastic Optimization via Nested Variance Reduction
  Junyu Zhang and Lin Xiao
  SIAM Journal on Optimization, 31(2):1131-1157, 2021.

• Adaptive stochastic variance reduction for subsampled Newton method with cubic regularization
  Junyu Zhang, Lin Xiao, and Shuzhong Zhang
  INFORMS Journal on Optimization, to appear, 2021.

2020

• Stochastic optimization with decision-dependent distributions
  Dmitriy Drusvyatskiy and Lin Xiao
  arXiv preprint, 2020.

• Statistical Adaptive Stochastic Gradient Methods
  Pengchuan Zhang, Hunter Lang, Qiang Liu, and Lin Xiao
  arXiv preprint, 2020.

• Stochastic variance-reduced prox-linear algorithms for nonconvex composite optimization
  Junyu Zhang and Lin Xiao
  arXiv preprint, 2020.

• Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization
  Hadrien Hendrikx, Lin Xiao, Sebastien Bubeck, Francis Bach, Laurent Massoulie
  Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4203-4227, 2020.

2019

• DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization
  Lin Xiao, Adams Wei Yu, Qihang Lin, Weizhu Chen
  Journal of Machine Learning Research, 20(43):1−58, 2019.

• Using Statistics to Automate Stochastic Optimization
  Hunter Lang, Lin Xiao, Pengchuan Zhang
  Advances in Neural Information Processing Systems 32 (NeurIPS 2019).

• Understanding the Role of Momentum in Stochastic Gradient Methods
  Igor Gitman, Hunter Lang, Pengchuan Zhang, Lin Xiao
  Advances in Neural Information Processing Systems 32 (NeurIPS 2019).

• Stochastic Composite Gradient Method with Incremental Variance Reduction
  Junyu Zhang, Lin Xiao Advances in Neural Information Processing Systems 32 (NeurIPS 2019).

2018

• Coupled Variational Bayes via Optimization Embedding
  Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song
  Advances in Neural Information Processing Systems 31 (NeurIPS 2018).

• Learning SMaLL Predictors
  Vikas Garg, Ofer Dekel, Lin Xiao
  Advances in Neural Information Processing Systems 31 (NeurIPS 2018).

• SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation
  Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song
  Proceedings of the 35th International Conference on Machine Learning, PMLR 80:1125-1134, 2018.

• Sparse Multi-Prototype Classification
  Vikas K. Garg, Lin Xiao, Ofer Dekel
  Proceedings of the Conference on Uncetainty in Artifical Intelligence (UAI), 2018.

• Communication-Efficient Distributed Optimization of Self-Concordant Empirical Loss
  Yuchen Zhang and Lin Xiao
  Large-Scale and Distributed Optimization, Giselsson and Rantzer (Editors), pages 289-341, Springer, 2018.

2017

• Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization
  Yuchen Zhang and Lin Xiao
  Journal of Machine Learning Research, 18(84):1-42, 2017.

• Variational Gram functions: convex analysis and optimization
  Amin Jalali, Maryam Fazel, Lin Xiao
  SIAM Journal on Optimization, 27(4):634-2661, 2017.

• A Randomized Nonmonotone Block Proximal Gradient Method for a Class of Structured Nonlinear Programming
  Zhaosong Lu and Lin Xiao
  SIAM Journal on Numerical Analysis, 55(6):2930-2955, 2017.

• Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes
  Jianshu Chen, Chong Wang, Lin Xiao, Ji He, Lihong Li, Li Deng
  Advances in Neural Information Processing Systems 30 (NIPS 2017).

• Stochastic Variance Reduction Methods for Policy Evaluation
  Simon S. Du, Jianshu Chen, Lihong Li, Lin Xiao, Dengyong Zhou
  Proceedings of the 34th International Conference on Machine Learning, PMLR 70:1049-1058, 2017.

• Exploiting Strong Convexity from Data with Primal-Dual First-Order Algorithms
  Jialei Wang and Lin Xiao
  Proceedings of the 34th International Conference on Machine Learning, PMLR 70:3694-3702, 2017.

2015

• An Accelerated Randomized Proximal Coordinate Gradient Method and its Application to Regularized Empirical Risk Minimization
  Qihang Lin, Zhaosong Lu, and Lin Xiao
  SIAM Journal on Optimization, 25(4):2244–2273, 2015.

• On the Complexity Analysis of Randomized Block-Coordinate Descent Methods
  Zhaosong Lu and Lin Xiao
  Mathematical Programming, Series A, 152(1-2):15-642, 2015.

• An adaptive accelerated proximal gradient method and its homotopy continuation for sparse optimization
  Qihang Lin and Lin Xiao
  Computational Optimization and Applications, 60(3):633-674, 2015.

• End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture
  Jianshu Chen, Ji He, Yelong Shen, Lin Xiao, Xiaodong He, Jianfeng Gao, Xinying Song, Li Deng
  Advances in Neural Information Processing Systems 28 (NIPS 2015).

• Scaling Up Stochastic Dual Coordinate Ascent
  Kenneth Tran, Saghar Hosseini, Lin Xiao, Thomas Finley, Mikhail Bilenko
  Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2015.

• Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization
  Yuchen Zhang and Lin Xiao
  Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:353-361, 2015.

• DiSCO: Distributed Optimization for Self-Concordant Empirical Loss
  Yuchen Zhang and Lin Xiao
  Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:362-370, 2015.