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Mingrui Liu
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2020 – today
- 2024
- [j8]Biao Guo, Mingrui Liu
, Qian He
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 - [i31]Jie Hao, Xiaochuan Gong, Mingrui Liu:
Bilevel Optimization under Unbounded Smoothness: A New Algorithm and Convergence Analysis. CoRR abs/2401.09587 (2024) - [i30]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) - [i29]Xiaochuan Gong, Jie Hao, Mingrui Liu:
An Accelerated Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness. CoRR abs/2409.19212 (2024) - [i28]Mingrui Liu, Sixiao Zhang, Cheng Long:
Mask-based Membership Inference Attacks for Retrieval-Augmented Generation. CoRR abs/2410.20142 (2024) - [i27]Michael Crawshaw, Mingrui Liu:
Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis. CoRR abs/2410.23131 (2024) - [i26]Mingrui Liu, Sixiao Zhang, Cheng Long:
Facet-Aware Multi-Head Mixture-of-Experts Model for Sequential Recommendation. CoRR abs/2411.01457 (2024) - 2023
- [j7]Shuhai Wang
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
A Generalized Propensity Learning Framework for Unbiased Post-Click Conversion Rate Estimation. CIKM 2023: 3554-3563 - [c30]Yubo Liu
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
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
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
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
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
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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