| 2026 | COLT | Tight Bounds for Logistic Regression with Large Stepsize Gradient Descent in Low Dimension. | Michael Crawshaw, Mingrui Liu |
| 2026 | KDD | Wukong Framework for Not Safe For Work Detection in Text-to-Image Systems. | Mingrui Liu, Sixiao Zhang, Cheng Long |
| 2025 | FAST | FlacIO: Flat and Collective I/O for Container Image Service. | Yubo Liu, Hongbo Li, Mingrui Liu, Rui Jing, Jian Guo, Bo Zhang, Hanjun Guo, Yuxin Ren, Ning Jia |
| 2025 | ICLR | Complexity Lower Bounds of Adaptive Gradient Algorithms for Non-convex Stochastic Optimization under Relaxed Smoothness. | Michael Crawshaw, Mingrui Liu |
| 2025 | ICLR | Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression. | Michael Crawshaw, Blake Woodworth, Mingrui Liu |
| 2025 | ICML | Constant Stepsize Local GD for Logistic Regression: Acceleration by Instability. | Michael Crawshaw, Blake Woodworth, Mingrui Liu |
| 2025 | WWW | Mask-based Membership Inference Attacks for Retrieval-Augmented Generation. | Mingrui Liu, Sixiao Zhang, Cheng Long |
| 2025 | WSDM | Facet-Aware Multi-Head Mixture-of-Experts Model for Sequential Recommendation. | Mingrui Liu, Sixiao Zhang, Cheng Long |
| 2024 | AAAI | Algorithmic Foundation of Federated Learning with Sequential Data. | Mingrui Liu |
| 2024 | FAST | Optimizing File Systems on Heterogeneous Memory by Integrating DRAM Cache with Virtual Memory Management. | Yubo Liu, Yuxin Ren, Mingrui Liu, Hongbo Li, Hanjun Guo, Xie Miao, Xinwei Hu, Haibo Chen |
| 2024 | ICLR | Bilevel Optimization under Unbounded Smoothness: A New Algorithm and Convergence Analysis. | Jie Hao, Xiaochuan Gong, Mingrui Liu |
| 2024 | ICML | Provable Benefits of Local Steps in Heterogeneous Federated Learning for Neural Networks: A Feature Learning Perspective. | Yajie Bao, Michael Crawshaw, Mingrui Liu |
| 2024 | ICML | A Nearly Optimal Single Loop Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness. | Xiaochuan Gong, Jie Hao, Mingrui Liu |
| 2024 | ICRA | LESS-Map: Lightweight and Evolving Semantic Map in Parking Lots for Long-term Self-Localization. | Mingrui Liu, Xinyang Tang, Yeqiang Qian, Jiming Chen, Liang Li |
| 2023 | CIKM | A Generalized Propensity Learning Framework for Unbiased Post-Click Conversion Rate Estimation. | Yuqing Zhou, Tianshu Feng, Mingrui Liu, Ziwei Zhu |
| 2023 | ICLR | EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data. | Michael Crawshaw, Yajie Bao, Mingrui Liu |
| 2023 | UAI | AUC Maximization in Imbalanced Lifelong Learning. | Xiangyu Zhu, Jie Hao, Yunhui Guo, Mingrui Liu |
| 2022 | ALT | On the Last Iterate Convergence of Momentum Methods. | Xiaoyu Li, Mingrui Liu, Francesco Orabona |
| 2022 | ALT | On the Initialization for Convex-Concave Min-max Problems. | Mingrui Liu, Francesco Orabona |
| 2022 | ICANN | F-Measure Optimization for Multi-class, Imbalanced Emotion Classification Tasks. | Toki Tahmid Inan, Mingrui Liu, Amarda Shehu |
| 2022 | ICML | Fast Composite Optimization and Statistical Recovery in Federated Learning. | Yajie Bao, Michael Crawshaw, Shan Luo, Mingrui Liu |
| 2020 | ICASSP | Improving Efficiency in Large-Scale Decentralized Distributed Training. | 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 | ICLR | 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 |
| 2020 | ICLR | Stochastic AUC Maximization with Deep Neural Networks. | Mingrui Liu, Zhuoning Yuan, Yiming Ying, Tianbao Yang |
| 2020 | ICML | Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks. | Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang |
| 2018 | ICML | Fast Stochastic AUC Maximization with O(1/n)-Convergence Rate. | Mingrui Liu, Xiaoxuan Zhang, Zaiyi Chen, Xiaoyu Wang, Tianbao Yang |