I am an associate professor at the Computer Science Department at the University of Iowa. I was a researcher at NEC Laboratories America, Inc. Before that, I was a Machine Learning Researcher at
GE Global Research. I received my Ph.D. degree in Computer Science from Michigan State University in 2012.
Here is my Google Scholar Citations.
- (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.
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