Liu, Mingrui

36 publications

NeurIPS 2025 Adaptive Algorithms with Sharp Convergence Rates for Stochastic Hierarchical Optimization Xiaochuan Gong, Jie Hao, Mingrui Liu

ICLR 2025 Complexity Lower Bounds of Adaptive Gradient Algorithms for Non-Convex Stochastic Optimization Under Relaxed Smoothness Michael Crawshaw, Mingrui Liu

ICML 2025 Constant Stepsize Local GD for Logistic Regression: Acceleration by Instability Michael Crawshaw, Blake Woodworth, Mingrui Liu

ICLR 2025 Local Steps Speed up Local GD for Heterogeneous Distributed Logistic Regression Michael Crawshaw, Blake Woodworth, Mingrui Liu

ICML 2024 A Nearly Optimal Single Loop Algorithm for Stochastic Bilevel Optimization Under Unbounded Smoothness Xiaochuan Gong, Jie Hao, Mingrui Liu

AAAI 2024 Algorithmic Foundation of Federated Learning with Sequential Data Mingrui Liu

NeurIPS 2024 An Accelerated Algorithm for Stochastic Bilevel Optimization Under Unbounded Smoothness Xiaochuan Gong, Jie Hao, Mingrui Liu

ICLR 2024 Bilevel Optimization Under Unbounded Smoothness: A New Algorithm and Convergence Analysis Jie Hao, Xiaochuan Gong, Mingrui Liu

NeurIPS 2024 Federated Learning Under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis Michael Crawshaw, Mingrui Liu

ICML 2024 Provable Benefits of Local Steps in Heterogeneous Federated Learning for Neural Networks: A Feature Learning Perspective Yajie Bao, Michael Crawshaw, Mingrui Liu

UAI 2023 AUC Maximization in Imbalanced Lifelong Learning Xiangyu Zhu, Jie Hao, Yunhui Guo, Mingrui Liu

NeurIPS 2023 Bilevel Coreset Selection in Continual Learning: A New Formulation and Algorithm Jie Hao, Kaiyi Ji, Mingrui Liu

ICLR 2023 EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data Michael Crawshaw, Yajie Bao, Mingrui Liu

NeurIPS 2023 Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds Michael Crawshaw, Yajie Bao, Mingrui Liu

NeurIPS 2023 Global Convergence Analysis of Local SGD for Two-Layer Neural Network Without Overparameterization Yajie Bao, Amarda Shehu, Mingrui Liu

NeurIPS 2022 A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks Mingrui Liu, Zhenxun Zhuang, Yunwen Lei, Chunyang Liao

ICML 2022 Fast Composite Optimization and Statistical Recovery in Federated Learning Yajie Bao, Michael Crawshaw, Shan Luo, Mingrui Liu

ALT 2022 On the Initialization for Convex-Concave Min-Max Problems Mingrui Liu, Francesco Orabona

ALT 2022 On the Last Iterate Convergence of Momentum Methods Xiaoyu Li, Mingrui Liu, Francesco Orabona

NeurIPS 2022 Robustness to Unbounded Smoothness of Generalized SignSGD Michael Crawshaw, Mingrui Liu, Francesco Orabona, Wei Zhang, Zhenxun Zhuang

TMLR 2022 Understanding AdamW Through Proximal Methods and Scale-Freeness Zhenxun Zhuang, Mingrui Liu, Ashok Cutkosky, Francesco Orabona

NeurIPS 2022 Will Bilevel Optimizers Benefit from Loops Kaiyi Ji, Mingrui Liu, Yingbin Liang, Lei Ying

JMLR 2021 First-Order Convergence Theory for Weakly-Convex-Weakly-Concave Min-Max Problems Mingrui Liu, Hassan Rafique, Qihang Lin, Tianbao Yang

NeurIPS 2021 Generalization Guarantee of SGD for Pairwise Learning Yunwen Lei, Mingrui Liu, Yiming Ying

NeurIPS 2020 A Decentralized Parallel Algorithm for Training Generative Adversarial Nets Mingrui Liu, Wei Zhang, Youssef Mroueh, Xiaodong Cui, Jarret Ross, Tianbao Yang, Payel Das

ICLR 2020 Attacking Lifelong Learning Models with Gradient Reversion Yunhui Guo, Mingrui Liu, Yandong Li, Liqiang Wang, Tianbao Yang, Tajana Rosing

ICML 2020 Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang

NeurIPS 2020 Improved Schemes for Episodic Memory-Based Lifelong Learning Yunhui Guo, Mingrui Liu, Tianbao Yang, Tajana Rosing

ICLR 2020 Stochastic AUC Maximization with Deep Neural Networks Mingrui Liu, Zhuoning Yuan, Yiming Ying, Tianbao Yang

ICLR 2020 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

NeurIPS 2018 Adaptive Negative Curvature Descent with Applications in Non-Convex Optimization Mingrui Liu, Zhe Li, Xiaoyu Wang, Jinfeng Yi, Tianbao Yang

NeurIPS 2018 Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions Mingrui Liu, Xiaoxuan Zhang, Lijun Zhang, Rong Jin, Tianbao Yang

ICML 2018 Fast Stochastic AUC Maximization with $O(1/n)$-Convergence Rate Mingrui Liu, Xiaoxuan Zhang, Zaiyi Chen, Xiaoyu Wang, Tianbao Yang

NeurIPS 2018 Faster Online Learning of Optimal Threshold for Consistent F-Measure Optimization Xiaoxuan Zhang, Mingrui Liu, Xun Zhou, Tianbao Yang

NeurIPS 2017 ADMM Without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization Yi Xu, Mingrui Liu, Qihang Lin, Tianbao Yang

NeurIPS 2017 Adaptive Accelerated Gradient Converging Method Under H\"olderian Error Bound Condition Mingrui Liu, Tianbao Yang