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Sharp Analysis of Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization


Feb 13, 2020
Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang


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Self-guided Approximate Linear Programs


Jan 09, 2020
Parshan Pakiman, Selvaprabu Nadarajah, Negar Soheili, Qihang Lin

* 57 pages, 7 figures 

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Model-Agnostic Linear Competitors -- When Interpretable Models Compete and Collaborate with Black-Box Models


Sep 23, 2019
Hassan Rafique, Tong Wang, Qihang Lin


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A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints


Aug 07, 2019
Qihang Lin, Selvaprabu Nadarajah, Negar Soheili, Tianbao Yang


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Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model


May 10, 2019
Tong Wang, Qihang Lin


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Stochastic Primal-Dual Algorithms with Faster Convergence than $O(1/\sqrt{T})$ for Problems without Bilinear Structure


Apr 23, 2019
Yan Yan, Yi Xu, Qihang Lin, Lijun Zhang, Tianbao Yang


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Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence


Nov 28, 2018
Yi Xu, Qi Qi, Qihang Lin, Rong Jin, Tianbao Yang


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Solving Weakly-Convex-Weakly-Concave Saddle-Point Problems as Weakly-Monotone Variational Inequality


Oct 24, 2018
Qihang Lin, Mingrui Liu, Hassan Rafique, Tianbao Yang


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A Unified Analysis of Stochastic Momentum Methods for Deep Learning


Aug 30, 2018
Yan Yan, Tianbao Yang, Zhe Li, Qihang Lin, Yi Yang

* In IJCAI, pp. 2955-2961. 2018 
* Previous Technical Report: arXiv:1604.03257 

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Block-Normalized Gradient Method: An Empirical Study for Training Deep Neural Network


Apr 23, 2018
Adams Wei Yu, Lei Huang, Qihang Lin, Ruslan Salakhutdinov, Jaime Carbonell


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RSG: Beating Subgradient Method without Smoothness and Strong Convexity


Apr 18, 2018
Tianbao Yang, Qihang Lin

* Accepted by JMLR (with minor revision, April 2018) 

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Prophit: Causal inverse classification for multiple continuously valued treatment policies


Feb 14, 2018
Michael T. Lash, Qihang Lin, W. Nick Street


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DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization


Oct 13, 2017
Lin Xiao, Adams Wei Yu, Qihang Lin, Weizhu Chen


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A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates


Jun 12, 2017
Tianbao Yang, Qihang Lin, Lijun Zhang

* This is the long version of our ICML 2017 paper 

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A budget-constrained inverse classification framework for smooth classifiers


Jun 08, 2017
Michael T. Lash, Qihang Lin, W. Nick Street, Jennifer G. Robinson


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Doubly Stochastic Primal-Dual Coordinate Method for Bilinear Saddle-Point Problem


Apr 12, 2017
Adams Wei Yu, Qihang Lin, Tianbao Yang


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Accelerated Stochastic Subgradient Methods under Local Error Bound Condition


Jan 17, 2017
Yi Xu, Qihang Lin, Tianbao Yang

* added some new results in this version 

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Generalized Inverse Classification


Jan 12, 2017
Michael T. Lash, Qihang Lin, W. Nick Street, Jennifer G. Robinson, Jeffrey Ohlmann

* Accepted to SDM 2017. Full paper + supplemental material 

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Bayesian Decision Process for Cost-Efficient Dynamic Ranking via Crowdsourcing


Dec 21, 2016
Xi Chen, Kevin Jiao, Qihang Lin

* Journal of Machine Learning Research 17 (2016) 1-40 

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Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than $O(1/ε)$


Nov 03, 2016
Yi Xu, Yan Yan, Qihang Lin, Tianbao Yang

* This is a long version of the paper accepted by NIPS 2016 

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Optimal Stochastic Strongly Convex Optimization with a Logarithmic Number of Projections


May 24, 2016
Jianhui Chen, Tianbao Yang, Qihang Lin, Lijun Zhang, Yi Chang

* Accepted by the 32th Conference on Uncertainty in Artificial Intelligence (UAI 2016) 

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Unified Convergence Analysis of Stochastic Momentum Methods for Convex and Non-convex Optimization


May 04, 2016
Tianbao Yang, Qihang Lin, Zhe Li

* Added some references and more empirical results 

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Stochastic subGradient Methods with Linear Convergence for Polyhedral Convex Optimization


Mar 31, 2016
Tianbao Yang, Qihang Lin

* This paper has been withdrawn by the author due to that it has been merged into arXiv manuscript arXiv:1512.03107 

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Distributed Stochastic Variance Reduced Gradient Methods and A Lower Bound for Communication Complexity


Jan 06, 2016
Jason D. Lee, Qihang Lin, Tengyu Ma, Tianbao Yang

* significant addition to both theory and experimental results 

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On Data Preconditioning for Regularized Loss Minimization


Sep 25, 2015
Tianbao Yang, Rong Jin, Shenghuo Zhu, Qihang Lin


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Fast Sparse Least-Squares Regression with Non-Asymptotic Guarantees


Jul 18, 2015
Tianbao Yang, Lijun Zhang, Qihang Lin, Rong Jin


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Statistical Decision Making for Optimal Budget Allocation in Crowd Labeling


Apr 24, 2014
Xi Chen, Qihang Lin, Dengyong Zhou

* 39 pages 

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Smoothing proximal gradient method for general structured sparse regression


Jun 29, 2012
Xi Chen, Qihang Lin, Seyoung Kim, Jaime G. Carbonell, Eric P. Xing

* Annals of Applied Statistics 2012, Vol. 6, No. 2, 719-752 
* Published in at http://dx.doi.org/10.1214/11-AOAS514 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

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Smoothing Proximal Gradient Method for General Structured Sparse Learning


Feb 14, 2012
Xi Chen, Qihang Lin, Seyoung Kim, Jaime G. Carbonell, Eric P. Xing

* arXiv admin note: substantial text overlap with arXiv:1005.4717 

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A Smoothing Stochastic Gradient Method for Composite Optimization


Jun 30, 2011
Qihang Lin, Xi Chen, Javier Pena

* working paper 

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