Mingrui Liu

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 70

Top Topics

Stochastic Gradient

Convergence Analysis

Top Venues

Publications

Jie Hao, Xiaochuan Gong, Mingrui Liu
Bilevel Optimization under Unbounded Smoothness: A New Algorithm and Convergence Analysis. ICLR (2024)
Toki Tahmid Inan, Mingrui Liu, Amarda Shehu
Beyond Single-Model Views for Deep Learning: Optimization versus Generalizability of Stochastic Optimization Algorithms. CoRR (2024)
Jie Hao, Xiaochuan Gong, Mingrui Liu
Bilevel Optimization under Unbounded Smoothness: A New Algorithm and Convergence Analysis. CoRR (2024)
Yubo Liu, Yuxin Ren, Mingrui Liu, Hongbo Li, Hanjun Guo, Xie Miao, Xinwei Hu, Haibo Chen
Optimizing File Systems on Heterogeneous Memory by Integrating DRAM Cache with Virtual Memory Management. FAST (2024)
Mingrui Liu
Algorithmic Foundation of Federated Learning with Sequential Data. AAAI (2024)
Mingrui Liu, Xinyang Tang, Yeqiang Qian, Jiming Chen, Liang Li
LESS-Map: Lightweight and Evolving Semantic Map in Parking Lots for Long-term Self-Localization. ICRA (2024)
Shuhai Wang, Xin Liu, Xiao Pan, Hanjie Xu, Mingrui Liu
Heterogeneous Graph Transformer for Meta-structure Learning with Application in Text Classification. ACM Trans. Web 17 (3) (2023)
Yuqing Zhou, Tianshu Feng, Mingrui Liu, Ziwei Zhu
A Generalized Propensity Learning Framework for Unbiased Post-Click Conversion Rate Estimation. CIKM (2023)
Zhonghai Lu, Rui Shi, Chao Guo, Mingrui Liu
Age Feature Enhanced Neural Network for RUL Estimation of Power Electronic Devices. ICPHM (2023)
Yajie Bao, Amarda Shehu, Mingrui Liu
Global Convergence Analysis of Local SGD for Two-layer Neural Network without Overparameterization. NeurIPS (2023)
Xiangyu Zhu, Jie Hao, Yunhui Guo, Mingrui Liu
AUC Maximization in Imbalanced Lifelong Learning. UAI (2023)
Michael Crawshaw, Yajie Bao, Mingrui Liu
Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds. NeurIPS (2023)
Yubo Liu, Yuxin Ren, Mingrui Liu, Hanjun Guo, Xie Miao, Xinwei Hu
Cache or Direct Access? Revitalizing Cache in Heterogeneous Memory File System. DIMES@SOSP (2023)
Michael Crawshaw, Yajie Bao, Mingrui Liu
EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data. CoRR (2023)
Jie Hao, Kaiyi Ji, Mingrui Liu
Bilevel Coreset Selection in Continual Learning: A New Formulation and Algorithm. NeurIPS (2023)
Michael Crawshaw, Yajie Bao, Mingrui Liu
EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data. ICLR (2023)
Yunwen Lei, Tao Sun, Mingrui Liu
Stability and Generalization for Minibatch SGD and Local SGD. CoRR (2023)
Mingrui Liu, Xiaogang Wang, Yang Wei, Jiayi Zhou, Rui Song, Zewei Lian
Infrared video behavior recognition algorithm based on AGX Xavier environment. SAFEPROCESS (2023)
Mingrui Liu, Xinyang Tang, Yeqiang Qian, Jiming Chen, Liang Li
LESS-Map: Lightweight and Evolving Semantic Map in Parking Lots for Long-term Self-Localization. CoRR (2023)
Zheng Wang, Mingrui Liu, Cheng Long, Qianru Zhang, Jiangneng Li, Chunyan Miao
On Inferring User Socioeconomic Status with Mobility Records. Big Data (2022)
Xiaoyu Li, Mingrui Liu, Francesco Orabona
On the Last Iterate Convergence of Momentum Methods. ALT (2022)
Mingrui Liu, Zhenxun Zhuang, Yunwei Lei, Chunyang Liao
A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks. CoRR (2022)
Kaiyi Ji, Mingrui Liu, Yingbin Liang, Lei Ying
Will Bilevel Optimizers Benefit from Loops. NeurIPS (2022)
Michael Crawshaw, Mingrui Liu, Francesco Orabona, Wei Zhang, Zhenxun Zhuang
Robustness to Unbounded Smoothness of Generalized SignSGD. CoRR (2022)
Mingrui Liu, Francesco Orabona
On the Initialization for Convex-Concave Min-max Problems. ALT (2022)
Yajie Bao, Michael Crawshaw, Shan Luo, Mingrui Liu
Fast Composite Optimization and Statistical Recovery in Federated Learning. ICML (2022)
Zhenxun Zhuang, Mingrui Liu, Ashok Cutkosky, Francesco Orabona
Understanding AdamW through Proximal Methods and Scale-Freeness. CoRR (2022)
Hassan Rafique, Mingrui Liu, Qihang Lin, Tianbao Yang
Weakly-convex-concave min-max optimization: provable algorithms and applications in machine learning. Optim. Methods Softw. 37 (3) (2022)
Mingrui Liu, Zhenxun Zhuang, Yunwen Lei, Chunyang Liao
A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks. NeurIPS (2022)
Zheng Wang, Mingrui Liu, Cheng Long, Qianru Zhang, Jiangneng Li, Chunyan Miao
On Inferring User Socioeconomic Status with Mobility Records. CoRR (2022)
Yajie Bao, Michael Crawshaw, Shan Luo, Mingrui Liu
Fast Composite Optimization and Statistical Recovery in Federated Learning. CoRR (2022)
Toki Tahmid Inan, Mingrui Liu, Amarda Shehu
F-Measure Optimization for Multi-class, Imbalanced Emotion Classification Tasks. ICANN (1) (2022)
Xue Li, Jia Peng, Tao Zhang, Mingrui Liu, Ying Fu, Haizheng Zhong, Jun Zhang
Quantum Dots-Functionalized Encryption Camera for Text Image Security Protection. Adv. Intell. Syst. 4 (12) (2022)
Michael Crawshaw, Mingrui Liu, Francesco Orabona, Wei Zhang, Zhenxun Zhuang
Robustness to Unbounded Smoothness of Generalized SignSGD. NeurIPS (2022)
Mahdi Beitollahi, Mingrui Liu, Ning Lu
DSFL: Dynamic Sparsification for Federated Learning. ICCSPA (2022)
Zhenxun Zhuang, Mingrui Liu, Ashok Cutkosky, Francesco Orabona
Understanding AdamW through Proximal Methods and Scale-Freeness. Trans. Mach. Learn. Res. 2022 (2022)
Kaiyi Ji, Mingrui Liu, Yingbin Liang, Lei Ying
Will Bilevel Optimizers Benefit from Loops. CoRR (2022)
Yunwen Lei, Mingrui Liu, Yiming Ying
Generalization Guarantee of SGD for Pairwise Learning. NeurIPS (2021)
Xiaodong Cui, Wei Zhang, Abdullah Kayi, Mingrui Liu, Ulrich Finkler, Brian Kingsbury, George Saon, David S. Kung
Asynchronous Decentralized Distributed Training of Acoustic Models. IEEE ACM Trans. Audio Speech Lang. Process. 29 (2021)
Xiaodong Cui, Wei Zhang, Abdullah Kayi, Mingrui Liu, Ulrich Finkler, Brian Kingsbury, George Saon, David S. Kung
Asynchronous Decentralized Distributed Training of Acoustic Models. CoRR (2021)
Mingrui Liu, Francesco Orabona
A Parameter-free Algorithm for Convex-concave Min-max Problems. CoRR (2021)
Wei Zhang, Mingrui Liu, Yu Feng, Xiaodong Cui, Brian Kingsbury, Yuhai Tu
Loss Landscape Dependent Self-Adjusting Learning Rates in Decentralized Stochastic Gradient Descent. CoRR (2021)
Mingrui Liu, Hassan Rafique, Qihang Lin, Tianbao Yang
First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems. J. Mach. Learn. Res. 22 (2021)
Xiaoyu Li, Mingrui Liu, Francesco Orabona
On the Last Iterate Convergence of Momentum Methods. CoRR (2021)
Mingrui Liu, Zhuoning Yuan, Yiming Ying, Tianbao Yang
Stochastic AUC Maximization with Deep Neural Networks. ICLR (2020)
Wei Zhang, Xiaodong Cui, Abdullah Kayi, Mingrui Liu, Ulrich Finkler, Brian Kingsbury, George Saon, Youssef Mroueh, Alper Buyuktosunoglu, Payel Das, David S. Kung, Michael Picheny
Improving Efficiency in Large-Scale Decentralized Distributed Training. ICASSP (2020)
Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks. CoRR (2020)
Mingrui Liu
The game of Cops and Robbers on directed graphs with forbidden subgraphs. CoRR (2020)
Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks. ICML (2020)
Mingrui Liu, Youssef Mroueh, Jerret Ross, Wei Zhang, Xiaodong Cui, Payel Das, Tianbao Yang
Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets. ICLR (2020)
Yunhui Guo, Mingrui Liu, Tianbao Yang, Tajana Rosing
Improved Schemes for Episodic Memory-based Lifelong Learning. NeurIPS (2020)
Mingrui Liu, Wei Zhang, Youssef Mroueh, Xiaodong Cui, Jarret Ross, Tianbao Yang, Payel Das
A Decentralized Parallel Algorithm for Training Generative Adversarial Nets. NeurIPS (2020)
Mingrui Liu, Wei Zhang, Francesco Orabona, Tianbao Yang
: A Stochastic Method with Adaptive Variance Reduction. CoRR (2020)
Wei Zhang, Xiaodong Cui, Abdullah Kayi, Mingrui Liu, Ulrich Finkler, Brian Kingsbury, George Saon, Youssef Mroueh, Alper Buyuktosunoglu, Payel Das, David S. Kung, Michael Picheny
Improving Efficiency in Large-Scale Decentralized Distributed Training. CoRR (2020)
Mingrui Liu, Zhuoning Yuan, Yiming Ying, Tianbao Yang
Stochastic AUC Maximization with Deep Neural Networks. CoRR (2019)
Mingrui Liu, Youssef Mroueh, Jerret Ross, Wei Zhang, Xiaodong Cui, Payel Das, Tianbao Yang
Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets. CoRR (2019)
Yunhui Guo, Mingrui Liu, Tianbao Yang, Tajana Rosing
Learning with Long-term Remembering: Following the Lead of Mixed Stochastic Gradient. CoRR (2019)
Yue Lin, He Wang, Bowen Yang, Mingrui Liu, Yin Li, Yuqing Zhang
A Blackboard Sharing Mechanism for Community Cyber Threat Intelligence Based on Multi-Agent System. ML4CS (2019)
Mingrui Liu
The Cop Number of Graphs with Forbidden Induced Subgraphs. CoRR (2019)
Mingrui Liu, Youssef Mroueh, Wei Zhang, Xiaodong Cui, Jerret Ross, Tianbao Yang, Payel Das
Decentralized Parallel Algorithm for Training Generative Adversarial Nets. CoRR (2019)
Mingrui Liu, Xiaoxuan Zhang, Zaiyi Chen, Xiaoyu Wang, Tianbao Yang
Fast Stochastic AUC Maximization with O(1/n)-Convergence Rate. ICML (2018)
Jalal K. Siddiqui, Elizabeth Baskin, Mingrui Liu, Carmen Z. Cantemir-Stone, Bofei Zhang, Russell Bonneville, Joseph P. McElroy, Kevin R. Coombes, Ewy A. Mathé
IntLIM: integration using linear models of metabolomics and gene expression data. BMC Bioinform. 19 (1) (2018)
Xiaoxuan Zhang, Mingrui Liu, Xun Zhou, Tianbao Yang
Faster Online Learning of Optimal Threshold for Consistent F-measure Optimization. NeurIPS (2018)
Hassan Rafique, Mingrui Liu, Qihang Lin, Tianbao Yang
Non-Convex Min-Max Optimization: Provable Algorithms and Applications in Machine Learning. CoRR (2018)
Mingrui Liu, Xiaoxuan Zhang, Lijun Zhang, Rong Jin, Tianbao Yang
Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions. NeurIPS (2018)
Mingrui Liu, Zhe Li, Xiaoyu Wang, Jinfeng Yi, Tianbao Yang
Adaptive Negative Curvature Descent with Applications in Non-convex Optimization. NeurIPS (2018)
Mingrui Liu, Xiaoxuan Zhang, Lijun Zhang, Rong Jin, Tianbao Yang
Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions. CoRR (2018)
Yi Xu, Mingrui Liu, Qihang Lin, Tianbao Yang
ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization. NIPS (2017)
Mingrui Liu, Tianbao Yang
Stochastic Non-convex Optimization with Strong High Probability Second-order Convergence. CoRR (2017)
Mingrui Liu, Tianbao Yang
Adaptive Accelerated Gradient Converging Method under H\"{o}lderian Error Bound Condition. NIPS (2017)