Mingrui Liu - RankMe

87 papers A* 28A 1B 2C 1Misc 1Journal 48Unranked 6

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Bilevel Optimization with Lower-Level Uniform Convexity: Theory and Algorithm. CoRR Yuman Wu, Xiaochuan Gong, Jie Hao, Mingrui Liu
2026	J	`jnl`	Tight Bounds for Logistic Regression with Large Stepsize Gradient Descent in Low Dimension. CoRR Michael Crawshaw, Mingrui Liu
2025	J	`jnl`	Adaptive Algorithms with Sharp Convergence Rates for Stochastic Hierarchical Optimization. CoRR Xiaochuan Gong, Jie Hao, Mingrui Liu
2025	J	`jnl`	An Exploration of Non-Euclidean Gradient Descent: Muon and its Many Variants. CoRR Michael Crawshaw, Chirag Modi, Mingrui Liu, Robert M. Gower
2025	J	`jnl`	BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining. CoRR Jie Hao, Rui Yu, Wei Zhang, Huixia Wang, Jie Xu, Mingrui Liu
2025	A*	`conf`	Complexity Lower Bounds of Adaptive Gradient Algorithms for Non-convex Stochastic Optimization under Relaxed Smoothness. ICLR Michael Crawshaw, Mingrui Liu
2025	J	`jnl`	Complexity Lower Bounds of Adaptive Gradient Algorithms for Non-convex Stochastic Optimization under Relaxed Smoothness. CoRR Michael Crawshaw, Mingrui Liu
2025	A*	`conf`	Constant Stepsize Local GD for Logistic Regression: Acceleration by Instability. ICML Michael Crawshaw, Blake Woodworth, Mingrui Liu
2025	J	`jnl`	Constant Stepsize Local GD for Logistic Regression: Acceleration by Instability. CoRR Michael Crawshaw, Blake Woodworth, Mingrui Liu
2025	A*	`conf`	Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression. ICLR Michael Crawshaw, Blake Woodworth, Mingrui Liu
2025	J	`jnl`	Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression. CoRR Michael Crawshaw, Blake Woodworth, Mingrui Liu
2025	J	`jnl`	Noise-Adaptive Layerwise Learning Rates: Accelerating Geometry-Aware Optimization for Deep Neural Network Training. CoRR Jie Hao, Xiaochuan Gong, Jie Xu, Zhengdao Wang, Mingrui Liu
2025	J	`jnl`	On the Convergence of Adam-Type Algorithm for Bilevel Optimization under Unbounded Smoothness. CoRR Xiaochuan Gong, Jie Hao, Mingrui Liu
2025	J	`jnl`	Personalized Federated Fine-tuning for Heterogeneous Data: An Automatic Rank Learning Approach via Two-Level LoRA. CoRR Jie Hao, Yuman Wu, Ali Payani, Myungjin Lee, Mingrui Liu
2025	J	`jnl`	ROEVO: Robust Organized Edge Feature-Based Visual Odometry Using RGB-D Cameras. IEEE Trans. Robotics Mingrui Liu, Xingxing Zuo, Renlang Huang, Minglei Zhao, Jiming Chen, Liang Li
2025	J	`jnl`	Why and How Auxiliary Tasks Improve JEPA Representations. CoRR Jiacan Yu, Siyi Chen, Mingrui Liu, Nono Horiuchi, Vladimir Braverman, Zicheng Xu, Dan Haramati, Randall Balestriero
2024	A*	`conf`	A Nearly Optimal Single Loop Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness. ICML Xiaochuan Gong, Jie Hao, Mingrui Liu
2024	J	`jnl`	A Nearly Optimal Single Loop Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness. CoRR Xiaochuan Gong, Jie Hao, Mingrui Liu
2024	A*	`conf`	Algorithmic Foundation of Federated Learning with Sequential Data. AAAI Mingrui Liu
2024	A*	`conf`	An Accelerated Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness. NeurIPS Xiaochuan Gong, Jie Hao, Mingrui Liu
2024	J	`jnl`	An Accelerated Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness. CoRR Xiaochuan Gong, Jie Hao, Mingrui Liu
2024	J	`jnl`	Beyond Single-Model Views for Deep Learning: Optimization versus Generalizability of Stochastic Optimization Algorithms. CoRR Toki Tahmid Inan, Mingrui Liu, Amarda Shehu
2024	A*	`conf`	Bilevel Optimization under Unbounded Smoothness: A New Algorithm and Convergence Analysis. ICLR Jie Hao, Xiaochuan Gong, Mingrui Liu
2024	J	`jnl`	Bilevel Optimization under Unbounded Smoothness: A New Algorithm and Convergence Analysis. CoRR Jie Hao, Xiaochuan Gong, Mingrui Liu
2024	A*	`conf`	Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis. NeurIPS Michael Crawshaw, Mingrui Liu
2024	J	`jnl`	Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis. CoRR Michael Crawshaw, Mingrui Liu
2024	A*	`conf`	LESS-Map: Lightweight and Evolving Semantic Map in Parking Lots for Long-term Self-Localization. ICRA Mingrui Liu, Xinyang Tang, Yeqiang Qian, Jiming Chen, Liang Li
2024	A*	`conf`	Provable Benefits of Local Steps in Heterogeneous Federated Learning for Neural Networks: A Feature Learning Perspective. ICML Yajie Bao, Michael Crawshaw, Mingrui Liu
2023	A	`conf`	AUC Maximization in Imbalanced Lifelong Learning. UAI Xiangyu Zhu, Jie Hao, Yunhui Guo, Mingrui Liu
2023	—	`conf`	Age Feature Enhanced Neural Network for RUL Estimation of Power Electronic Devices. ICPHM Zhonghai Lu, Rui Shi, Chao Guo, Mingrui Liu
2023	A*	`conf`	Bilevel Coreset Selection in Continual Learning: A New Formulation and Algorithm. NeurIPS Jie Hao, Kaiyi Ji, Mingrui Liu
2023	A*	`conf`	EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data. ICLR Michael Crawshaw, Yajie Bao, Mingrui Liu
2023	J	`jnl`	EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data. CoRR Michael Crawshaw, Yajie Bao, Mingrui Liu
2023	A*	`conf`	Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds. NeurIPS Michael Crawshaw, Yajie Bao, Mingrui Liu
2023	A*	`conf`	Global Convergence Analysis of Local SGD for Two-layer Neural Network without Overparameterization. NeurIPS Yajie Bao, Amarda Shehu, Mingrui Liu
2023	C	`conf`	Infrared video behavior recognition algorithm based on AGX Xavier environment. SAFEPROCESS Mingrui Liu, Xiaogang Wang, Yang Wei, Jiayi Zhou, Rui Song, Zewei Lian
2023	J	`jnl`	LESS-Map: Lightweight and Evolving Semantic Map in Parking Lots for Long-term Self-Localization. CoRR Mingrui Liu, Xinyang Tang, Yeqiang Qian, Jiming Chen, Liang Li
2023	J	`jnl`	Stability and Generalization for Minibatch SGD and Local SGD. CoRR Yunwen Lei, Tao Sun, Mingrui Liu
2022	A*	`conf`	A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks. NeurIPS Mingrui Liu, Zhenxun Zhuang, Yunwen Lei, Chunyang Liao
2022	J	`jnl`	A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks. CoRR Mingrui Liu, Zhenxun Zhuang, Yunwei Lei, Chunyang Liao
2022	—	`conf`	DSFL: Dynamic Sparsification for Federated Learning. ICCSPA Mahdi Beitollahi, Mingrui Liu, Ning Lu
2022	—	`conf`	F-Measure Optimization for Multi-class, Imbalanced Emotion Classification Tasks. ICANN (1) Toki Tahmid Inan, Mingrui Liu, Amarda Shehu
2022	A*	`conf`	Fast Composite Optimization and Statistical Recovery in Federated Learning. ICML Yajie Bao, Michael Crawshaw, Shan Luo, Mingrui Liu
2022	J	`jnl`	Fast Composite Optimization and Statistical Recovery in Federated Learning. CoRR Yajie Bao, Michael Crawshaw, Shan Luo, Mingrui Liu
2022	B	`conf`	On the Initialization for Convex-Concave Min-max Problems. ALT Mingrui Liu, Francesco Orabona
2022	B	`conf`	On the Last Iterate Convergence of Momentum Methods. ALT Xiaoyu Li, Mingrui Liu, Francesco Orabona
2022	J	`jnl`	Quantum Dots-Functionalized Encryption Camera for Text Image Security Protection. Adv. Intell. Syst. Xue Li, Jia Peng, Tao Zhang, Mingrui Liu, Ying Fu, Haizheng Zhong, Jun Zhang
2022	A*	`conf`	Robustness to Unbounded Smoothness of Generalized SignSGD. NeurIPS Michael Crawshaw, Mingrui Liu, Francesco Orabona, Wei Zhang, Zhenxun Zhuang
2022	J	`jnl`	Robustness to Unbounded Smoothness of Generalized SignSGD. CoRR Michael Crawshaw, Mingrui Liu, Francesco Orabona, Wei Zhang, Zhenxun Zhuang
2022	J	`jnl`	Understanding AdamW through Proximal Methods and Scale-Freeness. CoRR Zhenxun Zhuang, Mingrui Liu, Ashok Cutkosky, Francesco Orabona
2022	J	`jnl`	Understanding AdamW through Proximal Methods and Scale-Freeness. Trans. Mach. Learn. Res. Zhenxun Zhuang, Mingrui Liu, Ashok Cutkosky, Francesco Orabona
2022	J	`jnl`	Weakly-convex-concave min-max optimization: provable algorithms and applications in machine learning. Optim. Methods Softw. Hassan Rafique, Mingrui Liu, Qihang Lin, Tianbao Yang
2022	A*	`conf`	Will Bilevel Optimizers Benefit from Loops. NeurIPS Kaiyi Ji, Mingrui Liu, Yingbin Liang, Lei Ying
2022	J	`jnl`	Will Bilevel Optimizers Benefit from Loops. CoRR Kaiyi Ji, Mingrui Liu, Yingbin Liang, Lei Ying
2021	J	`jnl`	A Parameter-free Algorithm for Convex-concave Min-max Problems. CoRR Mingrui Liu, Francesco Orabona
2021	J	`jnl`	Asynchronous Decentralized Distributed Training of Acoustic Models. CoRR Xiaodong Cui, Wei Zhang, Abdullah Kayi, Mingrui Liu, Ulrich Finkler, Brian Kingsbury, George Saon, David S. Kung
2021	J	`jnl`	Asynchronous Decentralized Distributed Training of Acoustic Models. IEEE ACM Trans. Audio Speech Lang. Process. Xiaodong Cui, Wei Zhang, Abdullah Kayi, Mingrui Liu, Ulrich Finkler, Brian Kingsbury, George Saon, David S. Kung
2021	J	`jnl`	First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems. J. Mach. Learn. Res. Mingrui Liu, Hassan Rafique, Qihang Lin, Tianbao Yang
2021	A*	`conf`	Generalization Guarantee of SGD for Pairwise Learning. NeurIPS Yunwen Lei, Mingrui Liu, Yiming Ying
2021	J	`jnl`	Loss Landscape Dependent Self-Adjusting Learning Rates in Decentralized Stochastic Gradient Descent. CoRR Wei Zhang, Mingrui Liu, Yu Feng, Xiaodong Cui, Brian Kingsbury, Yuhai Tu
2021	J	`jnl`	On the Last Iterate Convergence of Momentum Methods. CoRR Xiaoyu Li, Mingrui Liu, Francesco Orabona
2020	A*	`conf`	A Decentralized Parallel Algorithm for Training Generative Adversarial Nets. NeurIPS Mingrui Liu, Wei Zhang, Youssef Mroueh, Xiaodong Cui, Jarret Ross, Tianbao Yang, Payel Das
2020	J	`jnl`	Adam CoRR Mingrui Liu, Wei Zhang, Francesco Orabona, Tianbao Yang
2020	A*	`conf`	Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks. ICML Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang
2020	J	`jnl`	Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks. CoRR Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang
2020	A*	`conf`	Improved Schemes for Episodic Memory-based Lifelong Learning. NeurIPS Yunhui Guo, Mingrui Liu, Tianbao Yang, Tajana Rosing
2020	Misc	`conf`	Improving Efficiency in Large-Scale Decentralized Distributed Training. ICASSP 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
2020	J	`jnl`	Improving Efficiency in Large-Scale Decentralized Distributed Training. CoRR 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
2020	A*	`conf`	Stochastic AUC Maximization with Deep Neural Networks. ICLR Mingrui Liu, Zhuoning Yuan, Yiming Ying, Tianbao Yang
2020	J	`jnl`	The game of Cops and Robbers on directed graphs with forbidden subgraphs. CoRR Mingrui Liu
2020	A*	`conf`	Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets. ICLR Mingrui Liu, Youssef Mroueh, Jerret Ross, Wei Zhang, Xiaodong Cui, Payel Das, Tianbao Yang
2019	—	`conf`	A Blackboard Sharing Mechanism for Community Cyber Threat Intelligence Based on Multi-Agent System. ML4CS Yue Lin, He Wang, Bowen Yang, Mingrui Liu, Yin Li, Yuqing Zhang
2019	J	`jnl`	Decentralized Parallel Algorithm for Training Generative Adversarial Nets. CoRR Mingrui Liu, Youssef Mroueh, Wei Zhang, Xiaodong Cui, Jerret Ross, Tianbao Yang, Payel Das
2019	J	`jnl`	Learning with Long-term Remembering: Following the Lead of Mixed Stochastic Gradient. CoRR Yunhui Guo, Mingrui Liu, Tianbao Yang, Tajana Rosing
2019	J	`jnl`	Stochastic AUC Maximization with Deep Neural Networks. CoRR Mingrui Liu, Zhuoning Yuan, Yiming Ying, Tianbao Yang
2019	J	`jnl`	The Cop Number of Graphs with Forbidden Induced Subgraphs. CoRR Mingrui Liu
2019	J	`jnl`	Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets. CoRR Mingrui Liu, Youssef Mroueh, Jerret Ross, Wei Zhang, Xiaodong Cui, Payel Das, Tianbao Yang
2018	A*	`conf`	Adaptive Negative Curvature Descent with Applications in Non-convex Optimization. NeurIPS Mingrui Liu, Zhe Li, Xiaoyu Wang, Jinfeng Yi, Tianbao Yang
2018	A*	`conf`	Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions. NeurIPS Mingrui Liu, Xiaoxuan Zhang, Lijun Zhang, Rong Jin, Tianbao Yang
2018	J	`jnl`	Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions. CoRR Mingrui Liu, Xiaoxuan Zhang, Lijun Zhang, Rong Jin, Tianbao Yang
2018	A*	`conf`	Fast Stochastic AUC Maximization with O(1/n)-Convergence Rate. ICML Mingrui Liu, Xiaoxuan Zhang, Zaiyi Chen, Xiaoyu Wang, Tianbao Yang
2018	A*	`conf`	Faster Online Learning of Optimal Threshold for Consistent F-measure Optimization. NeurIPS Xiaoxuan Zhang, Mingrui Liu, Xun Zhou, Tianbao Yang
2018	J	`jnl`	IntLIM: integration using linear models of metabolomics and gene expression data. BMC Bioinform. Jalal K. Siddiqui, Elizabeth Baskin, Mingrui Liu, Carmen Z. Cantemir-Stone, Bofei Zhang, Russell Bonneville, Joseph P. McElroy, Kevin R. Coombes, Ewy A. Mathé
2018	J	`jnl`	Non-Convex Min-Max Optimization: Provable Algorithms and Applications in Machine Learning. CoRR Hassan Rafique, Mingrui Liu, Qihang Lin, Tianbao Yang
2017	—	`conf`	ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization. NIPS Yi Xu, Mingrui Liu, Qihang Lin, Tianbao Yang
2017	—	`conf`	Adaptive Accelerated Gradient Converging Method under H\"{o}lderian Error Bound Condition. NIPS Mingrui Liu, Tianbao Yang
2017	J	`jnl`	Stochastic Non-convex Optimization with Strong High Probability Second-order Convergence. CoRR Mingrui Liu, Tianbao Yang