Tianbao Yang's homepage

(New! ): First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems.
Mingrui Liu, Hassan Rafique, Qihang Lin, Tianbao Yang
Accepted to JMLR
(New! ): Non-Convex Min-Max Optimization: Provable Algorithms and Applications in Machine Learning.
Hassan Rafique*, Mingrui Liu*, Qihang Lin, Tianbao Yang
In Optimization Methods and Software, 2020
(New! ): A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints.
Qihang Lin, Selvaprabu Nadarajah, Negar Soheili, Tianbao Yang
In JMLR, 2020
(New! ): Advancing Non-Convex and Constrained Learning: Challenges and Opportunities.
Tianbao Yang
AI Matters, 2019
High-dimensional model recovery from random sketched data by exploring intrinsic sparsity.
Tianbao Yang, Lijun Zhang, Qihang Lin, Shenghuo Zhu, Rong Jin
In ML, 2019
Accelerate Stochastic Subgradient Method by Leveraging Local Growth Condition.
Yi Xu, Qihang Lin, Tianbao Yang
In Analysis and Applications, 2019
Analysis of Nuclear Norm Regularization for Full-rank Matrix Completion.
Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou
In JMLR, 2019
A Simple Homotopy Proximal Mapping for Compressive Sensing.
Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu, Zhi-Hua Zhou
In Machine Learning (ML) Journal, 2018
Continuous amperometric hydrogen gas sensing in ionic liquids.
Yongan Tang, Jianxin He, Xiaoli Gao, Tianbao Yang, Xiangqun Zeng
In Analyst, 2018
RSG: Beating Subgradient Method without Smoothness and/or Strong Convexity.
Tianbao Yang, Qihang Lin
In Journal of Machine Learning Research (JMLR), 2018
Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement.
Jason Lee, Qihang Lin, Tengyu Ma, Tianbao Yang
In Journal of Machine Learning Research (JMLR), 2017
On Data Preconditioning for Regularized Loss Minimization [bibtex]
Tianbao Yang, Rong Jin, Shenghuo Zhu and Qihang Lin
In ML, 2015
Random Projections for Classification: A Recovery Approach
with Lijun Zhang, Mehrdad Mahdavi and Rong Jin and Shenghuo Zhu
In IEEE Transactions on Information Theory, 2014
An Efficient Primal-Dual Prox Method for Non-Smooth Optimization [bibtex]
Tianbao Yang, Mehrdad Mahdavi and Rong Jin and Shenghuo Zhu
In ML, 2014
Regret Bounded by Variation for Online Convex Optimization
Tianbao Yang, Mehrdad Mahdavi, Rong Jin and Shenghuo Zhu
In ML, 2014. The conference version received the Best Student Paper Award at COLT'12.
Combining a popularity-productivity stochastic block model with a discriminative content model for detecting general structures
Bian-fang Chai, Jian Yu, Cai-yan Jia, Tianbao Yang, Ya-wen Jiang
In Physical Review E., 2013
Improved Bound for the Nystrom's Method and its Application to Kernel Classification
with Rong Jin, Mehrdad Mahdavi and Yu-Feng Li and Zhi-Hua Zhou
In IEEE Information Theory, 2013
Trading Regret for Efficiency: Online Convex Optimization with Long Term Constraints
with Mehrdad Mahdavi and Rong Jin
In JMLR, 2012.
Online Multiple Kernel Classification
Steven Hoi, Rong Jin, Peilin Zhao, Tianbao Yang
In ML, 2012.
Detecting Communities and Their Evolutions in Dynamic Social Networks: A Bayesian Approach [Codes][bibtex]
Tianbao Yang, Yun Chi, Shenghuo Zhu, Yihong Gong, Rong Jin
In ML 2011