| 2026 | COLT | Faster Newton Methods for Convex and Nonconvex Optimization in Gradient Complexity. | Lesi Chen, Chengchang Liu, Luo Luo, Jingzhao Zhang |
| 2025 | AISTATS | Generalization Lower Bounds for GD and SGD in Smooth Stochastic Convex Optimization. | Peiyuan Zhang, Jiaye Teng, Jingzhao Zhang |
| 2025 | COLT | Solving Convex-Concave Problems with 풪(ε | Lesi Chen, Chengchang Liu, Luo Luo, Jingzhao Zhang |
| 2025 | COLT | Fast and Multiphase Rates for Nearest Neighbor Classifiers. | Pengkun Yang, Jingzhao Zhang |
| 2025 | ICLR | Second-Order Min-Max Optimization with Lazy Hessians. | Lesi Chen, Chengchang Liu, Jingzhao Zhang |
| 2025 | ICLR | From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency. | Kaiyue Wen, Huaqing Zhang, Hongzhou Lin, Jingzhao Zhang |
| 2025 | ICML | Scalable Model Merging with Progressive Layer-wise Distillation. | Jing Xu, Jiazheng Li, Jingzhao Zhang |
| 2025 | ICML | Task Generalization with Autoregressive Compositional Structure: Can Learning from D Tasks Generalize to DT Tasks? | Amirhesam Abedsoltan, Huaqing Zhang, Kaiyue Wen, Hongzhou Lin, Jingzhao Zhang, Mikhail Belkin |
| 2025 | ICML | Towards Black-Box Membership Inference Attack for Diffusion Models. | Jingwei Li, Jing Dong, Tianxing He, Jingzhao Zhang |
| 2025 | ICML | Understanding Nonlinear Implicit Bias via Region Counts in Input Space. | Jingwei Li, Jing Xu, Zifan Wang, Huishuai Zhang, Jingzhao Zhang |
| 2024 | COLT | On Finding Small Hyper-Gradients in Bilevel Optimization: Hardness Results and Improved Analysis. | Lesi Chen, Jing Xu, Jingzhao Zhang |
| 2024 | ICLR | A Quadratic Synchronization Rule for Distributed Deep Learning. | Xinran Gu, Kaifeng Lyu, Sanjeev Arora, Jingzhao Zhang, Longbo Huang |
| 2024 | ICML | Random Masking Finds Winning Tickets for Parameter Efficient Fine-tuning. | Jing Xu, Jingzhao Zhang |
| 2024 | UAI | Online Policy Optimization for Robust Markov Decision Process. | Jing Dong, Jingwei Li, Baoxiang Wang, Jingzhao Zhang |
| 2023 | ICLR | Benign Overfitting in Classification: Provably Counter Label Noise with Larger Models. | Kaiyue Wen, Jiaye Teng, Jingzhao Zhang |
| 2022 | ICML | Understanding the unstable convergence of gradient descent. | Kwangjun Ahn, Jingzhao Zhang, Suvrit Sra |
| 2022 | ICML | Beyond Worst-Case Analysis in Stochastic Approximation: Moment Estimation Improves Instance Complexity. | Jingzhao Zhang, Hongzhou Lin, Subhro Das, Suvrit Sra, Ali Jadbabaie |
| 2022 | ICML | Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective. | Jingzhao Zhang, Haochuan Li, Suvrit Sra, Ali Jadbabaie |
| 2021 | EMNLP | Exposure Bias versus Self-Recovery: Are Distortions Really Incremental for Autoregressive Text Generation? | Tianxing He, Jingzhao Zhang, Zhiming Zhou, James R. Glass |
| 2021 | ICLR | Coping with Label Shift via Distributionally Robust Optimisation. | Jingzhao Zhang, Aditya Krishna Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, Suvrit Sra |
| 2021 | ICML | Provably Efficient Algorithms for Multi-Objective Competitive RL. | Tiancheng Yu, Yi Tian, Jingzhao Zhang, Suvrit Sra |
| 2020 | ICLR | Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity. | Jingzhao Zhang, Tianxing He, Suvrit Sra, Ali Jadbabaie |
| 2020 | ICML | Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions. | Jingzhao Zhang, Hongzhou Lin, Stefanie Jegelka, Suvrit Sra, Ali Jadbabaie |