Yi Xu

Department of Computer Science The University of Iowa/Alibaba Group U.S. Inc.

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Research Interests
Machine Learning, Optimization, Learning Theory

Papers35 papers

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On the Convergence of Deep Networks with Sample Quadratic Overparameterization

Asaf Noy,Yi Xu, Yonathan Aflalo,Rong Jin

Bibtex

A Practical Online Method for Distributionally Deep Robust Optimization

Qi Qi, Zhishuai Guo,Yi Xu,Rong Jin,Tianbao Yang

Cited by1Bibtex

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

Yan Yan,Xu Yi,Lin Qihang,Liu Wei,Yang Tianbao

Cited by6Bibtex

Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization

Yan Yan,Yi Xu,Qihang Lin,Wei Liu,Tianbao Yang

NIPS 2020, (2020)

EIBibtex

WeMix: How to Better Utilize Data Augmentation

Yi Xu, Asaf Noy,Ming Lin,Qi Qian,Hao Li,Rong Jin

Bibtex

Towards Understanding Label Smoothing

Yi Xu, Yuanhong Xu, Qi Qian, Hao Li,Rong Jin

Bibtex

Stochastic Proximal Gradient Methods for Non-smooth Non-Convex Regularized Problems

Yi Xu,Rong Jin,Tianbao Yang

arXiv: Optimization and Control, (2019)

Cited by4Bibtex

Learning with Non-Convex Truncated Losses by SGD

Yi Xu,Shenghuo Zhu,Sen Yang, Chi Zhang,Rong Jin,Tianbao Yang

UAI, (2019): 244

Cited by13EIBibtex

Stochastic Primal Dual Algorithms with Faster Convergence than for Problems without Bilinear Structure

Yan Yan,Yi Xu, Qihang Lin, Lijun Zhang, Tianbao Yang

arXiv preprint arXiv:1904.10112, (2019)

Cited by5Bibtex

Stochastic Optimization for Non-convex Inf-Projection Problems

Yan Yan,Xu Yi,Zhang Lijun,Wang Xiaoyu,Yang Tianbao

ICML, pp.10660-10669, (2019)

EIBibtex

Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems

Yi Xu,Jing Rong,Tianbao Yang

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), (2019): 2626-2636

Cited by3EIBibtex

Accelerate Stochastic Subgradient Method by Leveraging Local Growth Condition

Yi Xu,Qihang Lin,Tianbao Yang

ANALYSIS AND APPLICATIONS, no. SP5 (2019): 773-818

Cited by2Bibtex

Accelerating convex optimization in machine learning by leveraging functional growth conditions

Yi Xu

(2019)

Bibtex

Stochastic Primal-Dual Algorithms with Faster Convergence than O(1/√T) for Problems without Bilinear Structure

Yan Yan,Yi Xu,Qihang Lin,Lijun Zhang,Tianbao Yang

arXiv: Learning, (2019)

EIBibtex

Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number

Zaiyi Chen,Yi Xu, Haoyuan Hu,Tianbao Yang

International Conference on Machine Learning, pp.1102-1111, (2019)

Cited by8EIBibtex

On the Convergence of (Stochastic) Gradient Descent with Extrapolation for Non-Convex Minimization

Yi Xu,Zhuoning Yuan,Sen Yang,Rong Jin,Tianbao Yang

IJCAI, pp.4003-4009, (2019)

Cited by3EIBibtex

Frank-Wolfe Method is Automatically Adaptive to Error Bound Condition

Yi Xu,Tianbao Yang

arXiv: Optimization and Control, (2018)

Cited by5Bibtex

Capturing pair-wise epistatic effects associated with three agronomic traits in barley

Yi Xu, Yajun Wu,Jixiang Wu

Genetica, no. 2 (2018): 161-170

WOSBibtex

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

Yi Xu, Qi Qi,Qihang Lin,Rong Jin,Tianbao Yang

international conference on machine learning, (2018)

Cited by12EIBibtex

First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time

Yi Xu,Jing Rong,Tianbao Yang

neural information processing systems, pp.5535-5545, (2018)

Cited by68EIBibtex

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Papers35 papers

Yi Xu