Mingrui Liu

Boston University

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My research interests are machine learning, optimization and learning theory. My goal is to design provably efficient algorithms for machine learning problems with strong empirical performance.

Papers27 papers

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Stochastic AUC Maximization with Deep Neural Networks

Mingrui Liu,Zhuoning Yuan,Yiming Ying,Tianbao Yang

ICLR, (2020)

Cited by8EIBibtex

Adam$^+$: A Stochastic Method with Adaptive Variance Reduction

Mingrui Liu, Wei Zhang, Francesco Orabona,Tianbao Yang

Bibtex

Improved Schemes for Episodic Memory-based Lifelong Learning

Yunhui Guo,Mingrui Liu,Tianbao Yang,Tajana Rosing

NIPS 2020, (2020)

Bibtex

A Decentralized Parallel Algorithm for Training Generative Adversarial Nets

Mingrui Liu,Wei Zhang,Youssef Mroueh,Xiaodong Cui, Jarret Ross,Tianbao Yang,Payel Das

NIPS 2020, (2020)

EIBibtex

Improved Schemes for Episodic Memory-based Lifelong Learning

Yunhui Guo,Mingrui Liu,Tianbao Yang,Tajana Rosing

NeurIPS, (2020)

EIBibtex

Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets

Mingrui Liu,Youssef Mroueh, Jerret Ross,Wei Zhang,Xiaodong Cui,Payel Das,Tianbao Yang

ICLR, (2020)

Cited by9EIBibtex

First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems

Liu Mingrui, Rafique Hassan,Lin Qihang,Yang Tianbao

Bibtex

Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks

Guo Zhishuai,Liu Mingrui,Yuan Zhuoning,Shen Li,Liu Wei,Yang Tianbao

ICML, pp.3864-3874, (2020)

EIBibtex

Improving Efficiency in Large-Scale Decentralized Distributed Training

Zhang Wei,Cui Xiaodong, Kayi Abdullah,Liu Mingrui,Finkler Ulrich,Kingsbury Brian,Saon George,Mroueh Youssef, Buyuktosunoglu Alper,Das Payel, Kung David,Picheny Michael

ICASSP, pp.3022-3026, (2020)

Cited by2EIBibtex

Decentralized Parallel Algorithm for Training Generative Adversarial Nets

Liu Mingrui,Mroueh Youssef,Zhang Wei,Cui Xiaodong,Yang Tianbao,Das Payel

Cited by2Bibtex

Fast Online Learning Algorithms for Large-Scale Imbalanced Data

Mingrui Liu

(2019)

Bibtex

Attacking Lifelong Learning Models with Gradient Reversion

Yunhui Guo,Mingrui Liu, Yandong Li, Liqiang Wang,Tianbao Yang, Tajana Rosing

(2019)

Bibtex

Learning with Long-term Remembering: Following the Lead of Mixed Stochastic Gradient

Guo Yunhui,Liu Mingrui,Yang Tianbao, Rosing Tajana

Bibtex

Fast Stochastic AUC Maximization with O(1/n)-Convergence Rate

Mingrui Liu, Xiaoxuan Zhang,Zaiyi Chen,Xiaoyu Wang,Tianbao Yang

ICML, pp.3195-3203, (2018)

Cited by7EIBibtex

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

Qihang Lin,Mingrui Liu, Hassan Rafique,Tianbao Yang

arXiv: Optimization and Control, (2018)

Cited by28Bibtex

Faster Online Learning of Optimal Threshold for Consistent F-measure Optimization

Mingrui Liu, Xiaoxuan Zhang,Xun Zhou,Tianbao Yang

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018), (2018)

Bibtex

Faster Online Learning of Optimal Threshold for Consistent F-measure Optimization

Xiaoxuan Zhang,Mingrui Liu,Xun Zhou,Tianbao Yang

NeurIPS, pp.3893-3903, (2018)

Cited by5EIBibtex

Adaptive Negative Curvature Descent with Applications in Non-convex Optimization

Mingrui Liu,Zhe Li,Xiaoyu Wang,Jinfeng Yi,Tianbao Yang

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018), (2018): 4858-4867

Cited by5EIBibtex

Fast Stochastic AUC Maximization with -Convergence Rate

Mingrui Liu, Xiaoxuan Zhang, Zaiyi Chen,Xiaoyu Wang,Tianbao Yang

International Conference on Machine Learning, pp.3189-3197, (2018)

Cited by21Bibtex

Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions

Mingrui Liu, Xiaoxuan Zhang,Lijun Zhang,Rong Jin,Tianbao Yang

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018), (2018): 4678-4689

Cited by6EIBibtex

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Papers27 papers

Mingrui Liu