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ORCIDMingrui Liu
This is just a disambiguation page, and is not intended to be the bibliography of an actual person. Any publication listed on this page has not been assigned to an actual author yet. If you know the true author of one of the publications listed below, you are welcome to contact us.
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2020 – today
- 2024
[j8]Biao Guo, Mingrui Liu
, Qian He, Ming Jiang:
Two-Stream Spatial-Temporal Auto-Encoder With Adversarial Training for Video Anomaly Detection. IEEE Access 12: 125881-125889 (2024)- [c38]Mingrui Liu:
Algorithmic Foundation of Federated Learning with Sequential Data. AAAI 2024: 22675 - [c37]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: 71-87 - [c36]Jie Hao, Xiaochuan Gong, Mingrui Liu:
Bilevel Optimization under Unbounded Smoothness: A New Algorithm and Convergence Analysis. ICLR 2024 - [c35]Yajie Bao, Michael Crawshaw, Mingrui Liu:
Provable Benefits of Local Steps in Heterogeneous Federated Learning for Neural Networks: A Feature Learning Perspective. ICML 2024 - [c34]Xiaochuan Gong, Jie Hao, Mingrui Liu:
A Nearly Optimal Single Loop Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness. ICML 2024 - [c33]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: 17990-17996 - [i28]Jie Hao, Xiaochuan Gong, Mingrui Liu:
Bilevel Optimization under Unbounded Smoothness: A New Algorithm and Convergence Analysis. CoRR abs/2401.09587 (2024) - [i27]Toki Tahmid Inan, Mingrui Liu, Amarda Shehu:
Beyond Single-Model Views for Deep Learning: Optimization versus Generalizability of Stochastic Optimization Algorithms. CoRR abs/2403.00574 (2024) - [i26]Xiaochuan Gong, Jie Hao, Mingrui Liu:
An Accelerated Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness. CoRR abs/2409.19212 (2024) - 2023
- [j7]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): 21:1-21:27 (2023) - [c32]Mingrui Liu, Xiaogang Wang, Yang Wei, Jiayi Zhou, Rui Song, Zewei Lian:
Infrared video behavior recognition algorithm based on AGX Xavier environment. SAFEPROCESS 2023: 1-5 - [c31]Yuqing Zhou, Tianshu Feng, Mingrui Liu, Ziwei Zhu:
A Generalized Propensity Learning Framework for Unbiased Post-Click Conversion Rate Estimation. CIKM 2023: 3554-3563 - [c30]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: 38-44 - [c29]Michael Crawshaw, Yajie Bao, Mingrui Liu:
EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data. ICLR 2023 - [c28]Zhonghai Lu, Rui Shi, Chao Guo, Mingrui Liu:
Age Feature Enhanced Neural Network for RUL Estimation of Power Electronic Devices. ICPHM 2023: 38-47 - [c27]Yajie Bao, Amarda Shehu, Mingrui Liu:
Global Convergence Analysis of Local SGD for Two-layer Neural Network without Overparameterization. NeurIPS 2023 - [c26]Michael Crawshaw, Yajie Bao, Mingrui Liu:
Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds. NeurIPS 2023 - [c25]Jie Hao, Kaiyi Ji, Mingrui Liu:
Bilevel Coreset Selection in Continual Learning: A New Formulation and Algorithm. NeurIPS 2023 - [c24]Xiangyu Zhu, Jie Hao, Yunhui Guo, Mingrui Liu:
AUC Maximization in Imbalanced Lifelong Learning. UAI 2023: 2574-2585 - [i25]Michael Crawshaw, Yajie Bao, Mingrui Liu:
EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data. CoRR abs/2302.07155 (2023) - [i24]Yunwen Lei, Tao Sun, Mingrui Liu:
Stability and Generalization for Minibatch SGD and Local SGD. CoRR abs/2310.01139 (2023) - [i23]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 abs/2310.07390 (2023) - 2022
- [j6]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) - [j5]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): 1087-1121 (2022) - [j4]Zhenxun Zhuang, Mingrui Liu, Ashok Cutkosky, Francesco Orabona:
Understanding AdamW through Proximal Methods and Scale-Freeness. Trans. Mach. Learn. Res. 2022 (2022) - [c23]Xiaoyu Li, Mingrui Liu, Francesco Orabona:
On the Last Iterate Convergence of Momentum Methods. ALT 2022: 699-717 - [c22]Mingrui Liu, Francesco Orabona:
On the Initialization for Convex-Concave Min-max Problems. ALT 2022: 743-767 - [c21]Zheng Wang, Mingrui Liu, Cheng Long, Qianru Zhang, Jiangneng Li, Chunyan Miao:
On Inferring User Socioeconomic Status with Mobility Records. IEEE Big Data 2022: 646-655 - [c20]Toki Tahmid Inan, Mingrui Liu, Amarda Shehu:
F-Measure Optimization for Multi-class, Imbalanced Emotion Classification Tasks. ICANN (1) 2022: 158-170 - [c19]Mahdi Beitollahi, Mingrui Liu, Ning Lu:
DSFL: Dynamic Sparsification for Federated Learning. ICCSPA 2022: 1-6 - [c18]Yajie Bao, Michael Crawshaw, Shan Luo, Mingrui Liu:
Fast Composite Optimization and Statistical Recovery in Federated Learning. ICML 2022: 1508-1536 - [c17]Michael Crawshaw, Mingrui Liu, Francesco Orabona, Wei Zhang, Zhenxun Zhuang:
Robustness to Unbounded Smoothness of Generalized SignSGD. NeurIPS 2022 - [c16]Kaiyi Ji, Mingrui Liu, Yingbin Liang, Lei Ying:
Will Bilevel Optimizers Benefit from Loops. NeurIPS 2022 - [c15]Mingrui Liu, Zhenxun Zhuang, Yunwen Lei, Chunyang Liao:
A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks. NeurIPS 2022 - [i22]Zhenxun Zhuang, Mingrui Liu, Ashok Cutkosky, Francesco Orabona:
Understanding AdamW through Proximal Methods and Scale-Freeness. CoRR abs/2202.00089 (2022) - [i21]Mingrui Liu, Zhenxun Zhuang, Yunwei Lei, Chunyang Liao:
A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks. CoRR abs/2205.05040 (2022) - [i20]Kaiyi Ji, Mingrui Liu, Yingbin Liang, Lei Ying:
Will Bilevel Optimizers Benefit from Loops. CoRR abs/2205.14224 (2022) - [i19]Yajie Bao, Michael Crawshaw, Shan Luo, Mingrui Liu:
Fast Composite Optimization and Statistical Recovery in Federated Learning. CoRR abs/2207.08204 (2022) - [i18]Michael Crawshaw, Mingrui Liu, Francesco Orabona, Wei Zhang, Zhenxun Zhuang:
Robustness to Unbounded Smoothness of Generalized SignSGD. CoRR abs/2208.11195 (2022) - [i17]Zheng Wang, Mingrui Liu, Cheng Long, Qianru Zhang, Jiangneng Li, Chunyan Miao:
On Inferring User Socioeconomic Status with Mobility Records. CoRR abs/2211.08200 (2022) - 2021
- [j3]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: 169:1-169:34 (2021) - [j2]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: 3565-3576 (2021) - [c14]Yunwen Lei, Mingrui Liu, Yiming Ying:
Generalization Guarantee of SGD for Pairwise Learning. NeurIPS 2021: 21216-21228 - [i16]Xiaoyu Li, Mingrui Liu, Francesco Orabona:
On the Last Iterate Convergence of Momentum Methods. CoRR abs/2102.07002 (2021) - [i15]Mingrui Liu, Francesco Orabona:
A Parameter-free Algorithm for Convex-concave Min-max Problems. CoRR abs/2103.00284 (2021) - [i14]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 abs/2110.11199 (2021) - [i13]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 abs/2112.01433 (2021) - 2020
- [c13]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: 3022-3026 - [c12]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 - [c11]Mingrui Liu, Zhuoning Yuan, Yiming Ying, Tianbao Yang:
Stochastic AUC Maximization with Deep Neural Networks. ICLR 2020 - [c10]Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang:
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks. ICML 2020: 3864-3874 - [c9]Yunhui Guo, Mingrui Liu, Tianbao Yang, Tajana Rosing:
Improved Schemes for Episodic Memory-based Lifelong Learning. NeurIPS 2020 - [c8]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 - [i12]Mingrui Liu:
The game of Cops and Robbers on directed graphs with forbidden subgraphs. CoRR abs/2001.09853 (2020) - [i11]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 abs/2002.01119 (2020) - [i10]Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang:
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks. CoRR abs/2005.02426 (2020) - [i9]Mingrui Liu, Wei Zhang, Francesco Orabona, Tianbao Yang:
Adam+: A Stochastic Method with Adaptive Variance Reduction. CoRR abs/2011.11985 (2020)
2010 – 2019
- 2019
- [c7]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: 253-270 - [i8]Mingrui Liu, Zhuoning Yuan, Yiming Ying, Tianbao Yang:
Stochastic AUC Maximization with Deep Neural Networks. CoRR abs/1908.10831 (2019) - [i7]Mingrui Liu:
The Cop Number of Graphs with Forbidden Induced Subgraphs. CoRR abs/1908.11478 (2019) - [i6]Yunhui Guo, Mingrui Liu, Tianbao Yang, Tajana Rosing:
Learning with Long-term Remembering: Following the Lead of Mixed Stochastic Gradient. CoRR abs/1909.11763 (2019) - [i5]Mingrui Liu, Youssef Mroueh, Wei Zhang, Xiaodong Cui, Jerret Ross, Tianbao Yang, Payel Das:
Decentralized Parallel Algorithm for Training Generative Adversarial Nets. CoRR abs/1910.12999 (2019) - [i4]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 abs/1912.11940 (2019) - 2018
- [j1]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): 81:1-81:12 (2018) - [c6]Mingrui Liu, Xiaoxuan Zhang, Zaiyi Chen, Xiaoyu Wang, Tianbao Yang:
Fast Stochastic AUC Maximization with O(1/n)-Convergence Rate. ICML 2018: 3195-3203 - [c5]Xiaoxuan Zhang, Mingrui Liu, Xun Zhou, Tianbao Yang:
Faster Online Learning of Optimal Threshold for Consistent F-measure Optimization. NeurIPS 2018: 3893-3903 - [c4]Mingrui Liu, Xiaoxuan Zhang, Lijun Zhang, Rong Jin, Tianbao Yang:
Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions. NeurIPS 2018: 4683-4694 - [c3]Mingrui Liu, Zhe Li, Xiaoyu Wang, Jinfeng Yi, Tianbao Yang:
Adaptive Negative Curvature Descent with Applications in Non-convex Optimization. NeurIPS 2018: 4858-4867 - [i3]Mingrui Liu, Xiaoxuan Zhang, Lijun Zhang, Rong Jin, Tianbao Yang:
Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions. CoRR abs/1805.04577 (2018) - [i2]Hassan Rafique, Mingrui Liu, Qihang Lin, Tianbao Yang:
Non-Convex Min-Max Optimization: Provable Algorithms and Applications in Machine Learning. CoRR abs/1810.02060 (2018) - 2017
- [c2]Yi Xu, Mingrui Liu, Qihang Lin, Tianbao Yang:
ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization. NIPS 2017: 1267-1277 - [c1]Mingrui Liu, Tianbao Yang:
Adaptive Accelerated Gradient Converging Method under H\"{o}lderian Error Bound Condition. NIPS 2017: 3104-3114 - [i1]Mingrui Liu, Tianbao Yang:
Stochastic Non-convex Optimization with Strong High Probability Second-order Convergence. CoRR abs/1710.09447 (2017)
Coauthor Index
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