Luo, Zhi-Quan

38 publications

ICLR 2025 Adam-Mini: Use Fewer Learning Rates to Gain More Yushun Zhang, Congliang Chen, Ziniu Li, Tian Ding, Chenwei Wu, Diederik P Kingma, Yinyu Ye, Zhi-Quan Luo, Ruoyu Sun

ICLR 2025 Preserving Diversity in Supervised Fine-Tuning of Large Language Models Ziniu Li, Congliang Chen, Tian Xu, Zeyu Qin, Jiancong Xiao, Zhi-Quan Luo, Ruoyu Sun

ICML 2025 ROS: A GNN-Based Relax-Optimize-and-Sample Framework for Max-$k$-Cut Problems Yeqing Qiu, Ye Xue, Akang Wang, Yiheng Wang, Qingjiang Shi, Zhi-Quan Luo

NeurIPS 2025 Scalable Exploration via Ensemble++ Yingru Li, Jiawei Xu, Baoxiang Wang, Zhi-Quan Luo

ICLRW 2025 Scalable Thompson Sampling via Ensemble++ Yingru Li, Jiawei Xu, Baoxiang Wang, Zhi-Quan Luo

ICMLW 2024 Adam-Mini: Use Fewer Learning Rates to Gain More Yushun Zhang, Congliang Chen, Ziniu Li, Tian Ding, Chenwei Wu, Yinyu Ye, Zhi-Quan Luo, Ruoyu Sun

ICMLW 2024 Adaptive Foundation Models for Online Decisions: HyperAgent with Fast Incremental Uncertainty Estimation Yingru Li, Jiawei Xu, Zhi-Quan Luo

JMLR 2024 Bridging Distributional and Risk-Sensitive Reinforcement Learning with Provable Regret Bounds Hao Liang, Zhi-Quan Luo

NeurIPSW 2024 Entropic Distribution Matching for Supervised Fine-Tuning of LLMs: Less Overfitting and Better Diversity Ziniu Li, Congliang Chen, Tian Xu, Zeyu Qin, Jiancong Xiao, Ruoyu Sun, Zhi-Quan Luo

ICMLW 2024 GPT-HyperAgent: Scalable Uncertainty Estimation and Exploration for Foundation Model Decisions Yingru Li, Jiawei Xu, Zhi-Quan Luo

NeurIPSW 2024 GaLore-Mini: Low Rank Gradient Learning with Fewer Learning Rates Weihao Huang, Zhenyu Zhang, Yushun Zhang, Zhi-Quan Luo, Ruoyu Sun, Zhangyang Wang

ICML 2024 Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgent Yingru Li, Jiawei Xu, Lei Han, Zhi-Quan Luo

ICML 2024 ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models Ziniu Li, Tian Xu, Yushun Zhang, Zhihang Lin, Yang Yu, Ruoyu Sun, Zhi-Quan Luo

ICML 2024 Uniformly Stable Algorithms for Adversarial Training and Beyond Jiancong Xiao, Jiawei Zhang, Zhi-Quan Luo, Asuman E. Ozdaglar

NeurIPS 2024 Why Transformers Need Adam: A Hessian Perspective Yushun Zhang, Congliang Chen, Tian Ding, Ziniu Li, Ruoyu Sun, Zhi-Quan Luo

ICMLW 2024 Why Transformers Need Adam: A Hessian Perspective Yushun Zhang, Congliang Chen, Tian Ding, Ziniu Li, Ruoyu Sun, Zhi-Quan Luo

ICML 2023 A Distribution Optimization Framework for Confidence Bounds of Risk Measures Hao Liang, Zhi-Quan Luo

NeurIPSW 2023 Efficient and Scalable Reinforcement Learning via Hypermodel Yingru Li, Jiawei Xu, Zhi-Quan Luo

NeurIPS 2023 Imitation Learning from Imperfection: Theoretical Justifications and Algorithms Ziniu Li, Tian Xu, Zeyu Qin, Yang Yu, Zhi-Quan Luo

ICMLW 2023 Improving Adversarial Training for Multiple Perturbations Through the Lens of Uniform Stability Jiancong Xiao, Zeyu Qin, Yanbo Fan, Baoyuan Wu, Jue Wang, Zhi-Quan Luo

ICMLW 2023 Optimistic Thompson Sampling for No-Regret Learning in Unknown Games Yingru Li, Liangqi Liu, Wenqiang Pu, Zhi-Quan Luo

ICMLW 2023 PAC-Bayesian Adversarially Robust Generalization Bounds for Deep Neural Networks Jiancong Xiao, Ruoyu Sun, Zhi-Quan Luo

NeurIPS 2023 PAC-Bayesian Spectrally-Normalized Bounds for Adversarially Robust Generalization Jiancong Xiao, Ruoyu Sun, Zhi-Quan Luo

UAI 2023 Provably Efficient Adversarial Imitation Learning with Unknown Transitions Tian Xu, Ziniu Li, Yang Yu, Zhi-Quan Luo

ICMLW 2023 Regret Bounds for Risk-Sensitive Reinforcement Learning with Lipschitz Dynamic Risk Measures Hao Liang, Zhi-Quan Luo

ICCV 2023 Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networks Qingyan Meng, Mingqing Xiao, Shen Yan, Yisen Wang, Zhouchen Lin, Zhi-Quan Luo

NeurIPS 2022 Adam Can Converge Without Any Modification on Update Rules Yushun Zhang, Congliang Chen, Naichen Shi, Ruoyu Sun, Zhi-Quan Luo

ICLR 2022 Fast Generic Interaction Detection for Model Interpretability and Compression Tianjian Zhang, Feng Yin, Zhi-Quan Luo

ICLR 2022 HyperDQN: A Randomized Exploration Method for Deep Reinforcement Learning Ziniu Li, Yingru Li, Yushun Zhang, Tong Zhang, Zhi-Quan Luo

NeurIPSW 2022 Smoothed-SGDmax: A Stability-Inspired Algorithm to Improve Adversarial Generalization Jiancong Xiao, Jiawei Zhang, Zhi-Quan Luo, Asuman E. Ozdaglar

NeurIPS 2022 Stability Analysis and Generalization Bounds of Adversarial Training Jiancong Xiao, Yanbo Fan, Ruoyu Sun, Jue Wang, Zhi-Quan Luo

CVPR 2022 Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation Qingyan Meng, Mingqing Xiao, Shen Yan, Yisen Wang, Zhouchen Lin, Zhi-Quan Luo

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

AISTATS 2019 Direct Acceleration of SAGA Using Sampled Negative Momentum Kaiwen Zhou, Qinghua Ding, Fanhua Shang, James Cheng, Danli Li, Zhi-Quan Luo

NeurIPS 2014 Parallel Direction Method of Multipliers Huahua Wang, Arindam Banerjee, Zhi-Quan Luo

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

NeurIPS 2013 On the Linear Convergence of the Proximal Gradient Method for Trace Norm Regularization Ke Hou, Zirui Zhou, Anthony Man-Cho So, Zhi-Quan Luo

NeCo 1991 On the Convergence of the LMS Algorithm with Adaptive Learning Rate for Linear Feedforward Networks Zhi-Quan Luo