Hong, Mingyi

90 publications

TMLR 2026 RT2I-Bench: Evaluating Robustness of Text-to-Image Systems Against Adversarial Attacks Athanasios Glentis, Ioannis Tsaknakis, Jiangweizhi Peng, Xun Xian, Yihua Zhang, Gaowen Liu, Charles Fleming, Mingyi Hong

ICML 2025 BRiTE: Bootstrapping Reinforced Thinking Process to Enhance Language Model Reasoning Han Zhong, Yutong Yin, Shenao Zhang, Xiaojun Xu, Yuanxin Liu, Yifei Zuo, Zhihan Liu, Boyi Liu, Sirui Zheng, Hongyi Guo, Liwei Wang, Mingyi Hong, Zhaoran Wang

ICLR 2025 DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction Xinwei Zhang, Zhiqi Bu, Borja Balle, Mingyi Hong, Meisam Razaviyayn, Vahab Mirrokni

TMLR 2025 Distributed Hierarchical Decomposition Framework for Multimodal Timeseries Prediction Wei Ye, Prashant Khanduri, Jiangweizhi Peng, Feng Tian, Jun Gao, Jie Ding, Zhi-Li Zhang, Mingyi Hong

ICLR 2025 Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMs Siyan Zhao, Mingyi Hong, Yang Liu, Devamanyu Hazarika, Kaixiang Lin

NeurIPS 2025 InfantAgent-Next: A Multimodal Generalist Agent for Automated Computer Interaction Bin Lei, Weitai Kang, Zijian Zhang, Winson Chen, Xi Xie, Shan Zuo, Mimi Xie, Ali Payani, Mingyi Hong, Yan Yan, Caiwen Ding

ICML 2025 Inference-Time Alignment of Diffusion Models with Direct Noise Optimization Zhiwei Tang, Jiangweizhi Peng, Jiasheng Tang, Mingyi Hong, Fan Wang, Tsung-Hui Chang

ICLR 2025 Joint Reward and Policy Learning with Demonstrations and Human Feedback Improves Alignment Chenliang Li, Siliang Zeng, Zeyi Liao, Jiaxiang Li, Dongyeop Kang, Alfredo Garcia, Mingyi Hong

ICML 2025 On the Vulnerability of Applying Retrieval-Augmented Generation Within Knowledge-Intensive Application Domains Xun Xian, Ganghua Wang, Xuan Bi, Rui Zhang, Jayanth Srinivasa, Ashish Kundu, Charles Fleming, Mingyi Hong, Jie Ding

ICLRW 2025 Reinforcement Learning in Inference Time: A Perspective from Successive Policy Iterations Xinnan Zhang, Chenliang Li, Siliang Zeng, Jiaxiang Li, Zhongruo Wang, Songtao Lu, Alfredo Garcia, Mingyi Hong

ICML 2025 RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models Quan Wei, Chung-Yiu Yau, Hoi To Wai, Yang Zhao, Dongyeop Kang, Youngsuk Park, Mingyi Hong

ICML 2025 Towards LLM Unlearning Resilient to Relearning Attacks: A Sharpness-Aware Minimization Perspective and Beyond Chongyu Fan, Jinghan Jia, Yihua Zhang, Anil Ramakrishna, Mingyi Hong, Sijia Liu

AISTATS 2025 Understanding Inverse Reinforcement Learning Under Overparameterization: Non-Asymptotic Analysis and Global Optimality Ruijia Zhang, Siliang Zeng, Chenliang Li, Alfredo Garcia, Mingyi Hong

TMLR 2025 νSAM: Memory-Efficient Sharpness-Aware Minimization via Nuclear Norm Constraints Thomas Pethick, Parameswaran Raman, Lenon Minorics, Mingyi Hong, Shoham Sabach, Volkan Cevher

NeurIPS 2024 DOPPLER: Differentially Private Optimizers with Low-Pass Filter for Privacy Noise Reduction Xinwei Zhang, Zhiqi Bu, Mingyi Hong, Meisam Razaviyayn

NeurIPS 2024 Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models Yimeng Zhang, Xin Chen, Jinghan Jia, Yihua Zhang, Chongyu Fan, Jiancheng Liu, Mingyi Hong, Ke Ding, Sijia Liu

ICLR 2024 Demystifying Poisoning Backdoor Attacks from a Statistical Perspective Ganghua Wang, Xun Xian, Ashish Kundu, Jayanth Srinivasa, Xuan Bi, Mingyi Hong, Jie Ding

NeurIPSW 2024 DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction Xinwei Zhang, Zhiqi Bu, Borja Balle, Mingyi Hong, Meisam Razaviyayn, Vahab Mirrokni

ICLR 2024 Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach Xinwei Zhang, Zhiqi Bu, Steven Wu, Mingyi Hong

ICML 2024 EMC$^2$: Efficient MCMC Negative Sampling for Contrastive Learning with Global Convergence Chung-Yiu Yau, Hoi To Wai, Parameswaran Raman, Soumajyoti Sarkar, Mingyi Hong

TMLR 2024 GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Data Xinwei Zhang, Mingyi Hong, Jie Chen

NeurIPS 2024 Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM Alignment Jiaxiang Li, Siliang Zeng, Hoi-To Wai, Chenliang Li, Alfredo Garcia, Mingyi Hong

ICMLW 2024 Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM Alignment Jiaxiang Li, Siliang Zeng, Hoi To Wai, Chenliang Li, Alfredo Garcia, Mingyi Hong

TMLR 2024 Hybrid Federated Learning for Feature & Sample Heterogeneity: Algorithms and Implementation Xinwei Zhang, Wotao Yin, Mingyi Hong, Tianyi Chen

AISTATS 2024 Krylov Cubic Regularized Newton: A Subspace Second-Order Method with Dimension-Free Convergence Rate Ruichen Jiang, Parameswaran Raman, Shoham Sabach, Aryan Mokhtari, Mingyi Hong, Volkan Cevher

NeurIPSW 2024 LLM Alignment Through Successive Policy Re-Weighting (SPR) Xinnan Zhang, Siliang Zeng, Jiaxiang Li, Kaixiang Lin, Mingyi Hong

NeurIPSW 2024 Learning Reward and Policy Jointly from Demonstration and Preference Improves Alignment Chenliang Li, Siliang Zeng, Zeyi Liao, Jiaxiang Li, Dongyeop Kang, Alfredo Garcia, Mingyi Hong

ICML 2024 MADA: Meta-Adaptive Optimizers Through Hyper-Gradient Descent Kaan Ozkara, Can Karakus, Parameswaran Raman, Mingyi Hong, Shoham Sabach, Branislav Kveton, Volkan Cevher

NeurIPS 2024 Pre-Training Differentially Private Models with Limited Public Data Zhiqi Bu, Xinwei Zhang, Sheng Zha, Mingyi Hong, George Karypis

NeurIPS 2024 RAW: A Robust and Agile Plug-and-Play Watermark Framework for AI-Generated Images with Provable Guarantees Xun Xian, Ganghua Wang, Xuan Bi, Jayanth Srinivasa, Ashish Kundu, Mingyi Hong, Jie Ding

ICML 2024 Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark Yihua Zhang, Pingzhi Li, Junyuan Hong, Jiaxiang Li, Yimeng Zhang, Wenqing Zheng, Pin-Yu Chen, Jason D. Lee, Wotao Yin, Mingyi Hong, Zhangyang Wang, Sijia Liu, Tianlong Chen

NeurIPS 2024 SLTrain: A Sparse Plus Low Rank Approach for Parameter and Memory Efficient Pretraining Andi Han, Jiaxiang Li, Wei Huang, Mingyi Hong, Akiko Takeda, Pratik Jawanpuria, Bamdev Mishra

ICMLW 2024 Tuning-Free Alignment of Diffusion Models with Direct Noise Optimization Zhiwei Tang, Jiangweizhi Peng, Jiasheng Tang, Mingyi Hong, Fan Wang, Tsung-Hui Chang

NeurIPS 2024 Unraveling the Gradient Descent Dynamics of Transformers Bingqing Song, Boran Han, Shuai Zhang, Jie Ding, Mingyi Hong

CoRL 2023 A Bayesian Approach to Robust Inverse Reinforcement Learning Ran Wei, Siliang Zeng, Chenliang Li, Alfredo Garcia, Anthony D McDonald, Mingyi Hong

NeurIPS 2023 A Unified Detection Framework for Inference-Stage Backdoor Defenses Xun Xian, Ganghua Wang, Jayanth Srinivasa, Ashish Kundu, Xuan Bi, Mingyi Hong, Jie Ding

ICML 2023 FedAvg Converges to Zero Training Loss Linearly for Overparameterized Multi-Layer Neural Networks Bingqing Song, Prashant Khanduri, Xinwei Zhang, Jinfeng Yi, Mingyi Hong

ICML 2023 Linearly Constrained Bilevel Optimization: A Smoothed Implicit Gradient Approach Prashant Khanduri, Ioannis Tsaknakis, Yihua Zhang, Jia Liu, Sijia Liu, Jiawei Zhang, Mingyi Hong

ICMLW 2023 Robust Inverse Reinforcement Learning Through Bayesian Theory of Mind Ran Wei, Siliang Zeng, Chenliang Li, Alfredo Garcia, Anthony McDonald, Mingyi Hong

NeurIPS 2023 Selectivity Drives Productivity: Efficient Dataset Pruning for Enhanced Transfer Learning Yihua Zhang, Yimeng Zhang, Aochuan Chen, Jinghan Jia, Jiancheng Liu, Gaowen Liu, Mingyi Hong, Shiyu Chang, Sijia Liu

ICML 2023 Understanding Backdoor Attacks Through the Adaptability Hypothesis Xun Xian, Ganghua Wang, Jayanth Srinivasa, Ashish Kundu, Xuan Bi, Mingyi Hong, Jie Ding

NeurIPS 2023 VCC: Scaling Transformers to 128k Tokens or More by Prioritizing Important Tokens Zhanpeng Zeng, Cole Hawkins, Mingyi Hong, Aston Zhang, Nikolaos Pappas, Vikas Singh, Shuai Zheng

ICLR 2023 What Is Missing in IRM Training and Evaluation? Challenges and Solutions Yihua Zhang, Pranay Sharma, Parikshit Ram, Mingyi Hong, Kush R. Varshney, Sijia Liu

NeurIPS 2023 When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning Siliang Zeng, Chenliang Li, Alfredo Garcia, Mingyi Hong

NeurIPS 2022 A Stochastic Linearized Augmented Lagrangian Method for Decentralized Bilevel Optimization Songtao Lu, Siliang Zeng, Xiaodong Cui, Mark Squillante, Lior Horesh, Brian Kingsbury, Jia Liu, Mingyi Hong

ICML 2022 A Stochastic Multi-Rate Control Framework for Modeling Distributed Optimization Algorithms Xinwei Zhang, Mingyi Hong, Sairaj Dhople, Nicola Elia

NeurIPSW 2022 A Unified Framework to Understand Decentralized and Federated Optimization Algorithms: A Multi-Rate Feedback Control Perspective Xinwei Zhang, Nicola Elia, Mingyi Hong

NeurIPS 2022 Advancing Model Pruning via Bi-Level Optimization Yihua Zhang, Yuguang Yao, Parikshit Ram, Pu Zhao, Tianlong Chen, Mingyi Hong, Yanzhi Wang, Sijia Liu

NeurIPSW 2022 Building Large Machine Learning Models from Small Distributed Models: A Layer Matching Approach Xinwei Zhang, Bingqing Song, Mehrdad Honarkhah, Jie Ding, Mingyi Hong

ICLR 2022 Decentralized Learning for Overparameterized Problems: A Multi-Agent Kernel Approximation Approach Prashant Khanduri, Haibo Yang, Mingyi Hong, Jia Liu, Hoi To Wai, Sijia Liu

UAI 2022 Distributed Adversarial Training to Robustify Deep Neural Networks at Scale Gaoyuan Zhang, Songtao Lu, Yihua Zhang, Xiangyi Chen, Pin-Yu Chen, Quanfu Fan, Lee Martie, Lior Horesh, Mingyi Hong, Sijia Liu

NeurIPS 2022 Distributed Optimization for Overparameterized Problems: Achieving Optimal Dimension Independent Communication Complexity Bingqing Song, Ioannis Tsaknakis, Chung-Yiu Yau, Hoi-To Wai, Mingyi Hong

ICLR 2022 How to Robustify Black-Box ML Models? a Zeroth-Order Optimization Perspective Yimeng Zhang, Yuguang Yao, Jinghan Jia, Jinfeng Yi, Mingyi Hong, Shiyu Chang, Sijia Liu

NeurIPS 2022 Inducing Equilibria via Incentives: Simultaneous Design-and-Play Ensures Global Convergence Boyi Liu, Jiayang Li, Zhuoran Yang, Hoi-To Wai, Mingyi Hong, Yu Nie, Zhaoran Wang

L4DC 2022 Learning to Coordinate in Multi-Agent Systems: A Coordinated Actor-Critic Algorithm and Finite-Time Guarantees Siliang Zeng, Tianyi Chen, Alfredo Garcia, Mingyi Hong

NeurIPS 2022 Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees Siliang Zeng, Chenliang Li, Alfredo Garcia, Mingyi Hong

ICMLW 2022 Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees Siliang Zeng, Chenliang Li, Alfredo Garcia, Mingyi Hong

NeurIPSW 2022 On the Robustness of Deep Learning-Based MRI Reconstruction to Image Transformations Jinghan Jia, Mingyi Hong, Yimeng Zhang, Mehmet Akcakaya, Sijia Liu

ICML 2022 Revisiting and Advancing Fast Adversarial Training Through the Lens of Bi-Level Optimization Yihua Zhang, Guanhua Zhang, Prashant Khanduri, Mingyi Hong, Shiyu Chang, Sijia Liu

ICML 2022 Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy Xinwei Zhang, Xiangyi Chen, Mingyi Hong, Steven Wu, Jinfeng Yi

AISTATS 2021 Finding First-Order Nash Equilibria of Zero-Sum Games with the Regularized Nikaido-Isoda Function Ioannis Tsaknakis, Mingyi Hong

AISTATS 2021 Generalization Bounds for Stochastic Saddle Point Problems Junyu Zhang, Mingyi Hong, Mengdi Wang, Shuzhong Zhang

NeurIPS 2021 A Near-Optimal Algorithm for Stochastic Bilevel Optimization via Double-Momentum Prashant Khanduri, Siliang Zeng, Mingyi Hong, Hoi-To Wai, Zhaoran Wang, Zhuoran Yang

ICML 2021 Decentralized Riemannian Gradient Descent on the Stiefel Manifold Shixiang Chen, Alfredo Garcia, Mingyi Hong, Shahin Shahrampour

ICLR 2021 RMSProp Converges with Proper Hyper-Parameter Naichen Shi, Dawei Li, Mingyi Hong, Ruoyu Sun

NeurIPS 2021 STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning Prashant Khanduri, Pranay Sharma, Haibo Yang, Mingyi Hong, Jia Liu, Ketan Rajawat, Pramod Varshney

NeurIPS 2021 When Expressivity Meets Trainability: Fewer than $n$ Neurons Can Work Jiawei Zhang, Yushun Zhang, Mingyi Hong, Ruoyu Sun, Zhi-Quan Luo

NeurIPS 2020 Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms Xiangyi Chen, Tiancong Chen, Haoran Sun, Steven Z. Wu, Mingyi Hong

NeurIPS 2020 Finding Second-Order Stationary Points Efficiently in Smooth Nonconvex Linearly Constrained Optimization Problems Songtao Lu, Meisam Razaviyayn, Bo Yang, Kejun Huang, Mingyi Hong

ICML 2020 Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: Joint Gradient Estimation and Tracking Haoran Sun, Songtao Lu, Mingyi Hong

ICML 2020 Min-Max Optimization Without Gradients: Convergence and Applications to Black-Box Evasion and Poisoning Attacks Sijia Liu, Songtao Lu, Xiangyi Chen, Yao Feng, Kaidi Xu, Abdullah Al-Dujaili, Mingyi Hong, Una-May O’Reilly

NeurIPS 2020 Provably Efficient Neural GTD for Off-Policy Learning Hoi-To Wai, Zhuoran Yang, Zhaoran Wang, Mingyi Hong

NeurIPS 2020 Understanding Gradient Clipping in Private SGD: A Geometric Perspective Xiangyi Chen, Steven Z. Wu, Mingyi Hong

UAI 2019 On Fast Convergence of Proximal Algorithms for SQRT-Lasso Optimization: Don’t Worry About Its Nonsmooth Loss Function Xinguo Li, Haoming Jiang, Jarvis Haupt, Raman Arora, Han Liu, Mingyi Hong, Tuo Zhao

ICLR 2019 On the Convergence of a Class of Adam-Type Algorithms for Non-Convex Optimization Xiangyi Chen, Sijia Liu, Ruoyu Sun, Mingyi Hong

ICML 2019 PA-GD: On the Convergence of Perturbed Alternating Gradient Descent to Second-Order Stationary Points for Structured Nonconvex Optimization Songtao Lu, Mingyi Hong, Zhengdao Wang

NeurIPS 2019 Provably Global Convergence of Actor-Critic: A Case for Linear Quadratic Regulator with Ergodic Cost Zhuoran Yang, Yongxin Chen, Mingyi Hong, Zhaoran Wang

IJCAI 2019 Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective Kaidi Xu, Hongge Chen, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Mingyi Hong, Xue Lin

NeurIPS 2019 Variance Reduced Policy Evaluation with Smooth Function Approximation Hoi-To Wai, Mingyi Hong, Zhuoran Yang, Zhaoran Wang, Kexin Tang

NeurIPS 2019 ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization Xiangyi Chen, Sijia Liu, Kaidi Xu, Xingguo Li, Xue Lin, Mingyi Hong, David Cox

ICLR 2019 signSGD via Zeroth-Order Oracle Sijia Liu, Pin-Yu Chen, Xiangyi Chen, Mingyi Hong

ICML 2018 Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization over Networks Mingyi Hong, Meisam Razaviyayn, Jason Lee

NeurIPS 2018 Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization Hoi-To Wai, Zhuoran Yang, Zhaoran Wang, Mingyi Hong

AISTATS 2017 A Stochastic Nonconvex Splitting Method for Symmetric Nonnegative Matrix Factorization Songtao Lu, Mingyi Hong, Zhengdao Wang

ICML 2017 Prox-PDA: The Proximal Primal-Dual Algorithm for Fast Distributed Nonconvex Optimization and Learning over Networks Mingyi Hong, Davood Hajinezhad, Ming-Min Zhao

ICML 2017 Towards K-Means-Friendly Spaces: Simultaneous Deep Learning and Clustering Bo Yang, Xiao Fu, Nicholas D. Sidiropoulos, Mingyi Hong

AISTATS 2016 An Improved Convergence Analysis of Cyclic Block Coordinate Descent-Type Methods for Strongly Convex Minimization Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, Mingyi Hong

NeurIPS 2016 NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization Davood Hajinezhad, Mingyi Hong, Tuo Zhao, Zhaoran Wang

NeurIPS 2015 Improved Iteration Complexity Bounds of Cyclic Block Coordinate Descent for Convex Problems Ruoyu Sun, Mingyi Hong

NeurIPS 2014 Parallel Successive Convex Approximation for Nonsmooth Nonconvex Optimization Meisam Razaviyayn, Mingyi Hong, Zhi-Quan Luo, Jong-Shi Pang