Direct Runge-Kutta Discretization Achieves Acceleration. - Jingzhao Zhang; Aryan Mokhtari; Suvrit Sra; Ali Jadbabaie (NeurIPS, 2018)

R-SPIDER: A Fast Riemannian Stochastic Optimization Algorithm with Curvature Independent Rate. - Jingzhao Zhang; Hongyi Zhang; Suvrit Sra (CoRR, 2018)

A Probe Towards Understanding GAN and VAE Models. - Lu Mi; Macheng Shen; Jingzhao Zhang (CoRR, 2018)

Achieving Acceleration in Distributed Optimization via Direct Discretization of the Heavy-Ball ODE. - Jingzhao Zhang; César A. Uribe; Aryan Mokhtari; Ali Jadbabaie (ACC, 2019)

Acceleration in First Order Quasi-strongly Convex Optimization by ODE Discretization. - Jingzhao Zhang; Suvrit Sra; Ali Jadbabaie (CDC, 2019)

Quantifying Exposure Bias for Neural Language Generation. - Tianxing He; Jingzhao Zhang; Zhiming Zhou 001; James R. Glass (CoRR, 2019)

Analysis of Gradient Clipping and Adaptive Scaling with a Relaxed Smoothness Condition. - Jingzhao Zhang; Tianxing He; Suvrit Sra; Ali Jadbabaie (CoRR, 2019)

Why ADAM Beats SGD for Attention Models. - Jingzhao Zhang; Sai Praneeth Karimireddy; Andreas Veit; Seungyeon Kim 001; Sashank J. Reddi; Sanjiv Kumar; Suvrit Sra (CoRR, 2019)

Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity. - Jingzhao Zhang; Tianxing He; Suvrit Sra; Ali Jadbabaie (ICLR, 2020)

Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions. - Jingzhao Zhang; Hongzhou Lin; Stefanie Jegelka; Suvrit Sra; Ali Jadbabaie (ICML, 2020)

Why are Adaptive Methods Good for Attention Models? - Jingzhao Zhang; Sai Praneeth Karimireddy; Andreas Veit; Seungyeon Kim 001; Sashank J. Reddi; Sanjiv Kumar; Suvrit Sra (NeurIPS, 2020)

On Complexity of Finding Stationary Points of Nonsmooth Nonconvex Functions. - Jingzhao Zhang; Hongzhou Lin; Suvrit Sra; Ali Jadbabaie (CoRR, 2020)

Stochastic Optimization with Non-stationary Noise. - Jingzhao Zhang; Hongzhou Lin; Subhro Das; Suvrit Sra; Ali Jadbabaie (CoRR, 2020)

Exposure Bias versus Self-Recovery: Are Distortions Really Incremental for Autoregressive Text Generation? - Tianxing He; Jingzhao Zhang; Zhiming Zhou 001; James R. Glass (EMNLP (1), 2021)

Coping with Label Shift via Distributionally Robust Optimisation. - Jingzhao Zhang; Aditya Krishna Menon; Andreas Veit; Srinadh Bhojanapalli; Sanjiv Kumar; Suvrit Sra (ICLR, 2021)

Provably Efficient Algorithms for Multi-Objective Competitive RL. - Tiancheng Yu; Yi Tian; Jingzhao Zhang; Suvrit Sra (ICML, 2021)

Fast Federated Learning in the Presence of Arbitrary Device Unavailability. - Xinran Gu; Kaixuan Huang; Jingzhao Zhang; Longbo Huang (NeurIPS, 2021)

Complexity Lower Bounds for Nonconvex-Strongly-Concave Min-Max Optimization. - Haochuan Li; Yi Tian; Jingzhao Zhang; Ali Jadbabaie (NeurIPS, 2021)

On Convergence of Training Loss Without Reaching Stationary Points. - Jingzhao Zhang; Haochuan Li; Suvrit Sra; Ali Jadbabaie (CoRR, 2021)

Monitoring, Analyzing, and Modeling for Single Subsidence Basin in Coal Mining Areas Based on SAR Interferometry with L-Band Data. - Zhiyong Wang 010; Jingzhao Zhang; Yaran Yu; Jian Liu; Wei Liu; Na Jiang; Donge Guo (Sci. Program., 2021)

Understanding the unstable convergence of gradient descent. - Kwangjun Ahn; Jingzhao Zhang; Suvrit Sra (ICML, 2022)

Beyond Worst-Case Analysis in Stochastic Approximation: Moment Estimation Improves Instance Complexity. - Jingzhao Zhang; Hongzhou Lin; Subhro Das; Suvrit Sra; Ali Jadbabaie (ICML, 2022)

Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective. - Jingzhao Zhang; Haochuan Li; Suvrit Sra; Ali Jadbabaie (ICML, 2022)

Efficient Sampling on Riemannian Manifolds via Langevin MCMC. - Xiang Cheng; Jingzhao Zhang; Suvrit Sra (NeurIPS, 2022)

Detecting Electric Vehicle Battery Failure via Dynamic-VAE. - Haowei He; Jingzhao Zhang; Yanan Wang; Shaobo Huang; Chen Wang; Yang Zhang; Dongxu Guo; Guannan He; Minggao Ouyang (CoRR, 2022)

Minimax in Geodesic Metric Spaces: Sion's Theorem and Algorithms. - Peiyuan Zhang; Jingzhao Zhang; Suvrit Sra (CoRR, 2022)

Realistic Deep Learning May Not Fit Benignly. - Kaiyue Wen; Jiaye Teng; Jingzhao Zhang (CoRR, 2022)

Online Policy Optimization for Robust MDP. - Jing Dong 008; Jingwei Li; Baoxiang Wang 001; Jingzhao Zhang (CoRR, 2022)

Optimization Theory and Machine Learning Practice: Mind the Gap. - Jingzhao Zhang (2022)

Benign Overfitting in Classification: Provably Counter Label Noise with Larger Models. - Kaiyue Wen; Jiaye Teng; Jingzhao Zhang (ICLR, 2023)

Fast Conditional Mixing of MCMC Algorithms for Non-log-concave Distributions. - Xiang Cheng; Bohan Wang; Jingzhao Zhang; Yusong Zhu (NeurIPS, 2023)

Iteratively Learn Diverse Strategies with State Distance Information. - Wei Fu; Weihua Du; Jingwei Li; Sunli Chen; Jingzhao Zhang; Yi Wu 013 (NeurIPS, 2023)

On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective. - Zeke Xie; Zhiqiang Xu; Jingzhao Zhang; Issei Sato; Masashi Sugiyama (NeurIPS, 2023)

On Bilevel Optimization without Lower-level Strong Convexity. - Lesi Chen; Jing Xu 027; Jingzhao Zhang (CoRR, 2023)

Lower Generalization Bounds for GD and SGD in Smooth Stochastic Convex Optimization. - Peiyuan Zhang; Jiaye Teng; Jingzhao Zhang (CoRR, 2023)

Online Control with Adversarial Disturbance for Continuous-time Linear Systems. - Jingwei Li; Jing Dong 008; Baoxiang Wang 001; Jingzhao Zhang (CoRR, 2023)

Near-Optimal Fully First-Order Algorithms for Finding Stationary Points in Bilevel Optimization. - Lesi Chen; Yaohua Ma; Jingzhao Zhang (CoRR, 2023)

Two Phases of Scaling Laws for Nearest Neighbor Classifiers. - Pengkun Yang; Jingzhao Zhang (CoRR, 2023)

Sion's Minimax Theorem in Geodesic Metric Spaces and a Riemannian Extragradient Algorithm. - Peiyuan Zhang; Jingzhao Zhang; Suvrit Sra (SIAM J. Optim., 2023)

On Finding Small Hyper-Gradients in Bilevel Optimization: Hardness Results and Improved Analysis. - Lesi Chen; Jing Xu; Jingzhao Zhang (COLT, 2024)

A Quadratic Synchronization Rule for Distributed Deep Learning. - Xinran Gu; Kaifeng Lyu; Sanjeev Arora; Jingzhao Zhang; Longbo Huang (ICLR, 2024)

Random Masking Finds Winning Tickets for Parameter Efficient Fine-tuning. - Jing Xu; Jingzhao Zhang (ICML, 2024)

Online Control with Adversarial Disturbance for Continuous-time Linear Systems. - Jingwei Li; Jing Dong 008; Can Chang; Baoxiang Wang 001; Jingzhao Zhang (NeurIPS, 2024)

Functionally Constrained Algorithm Solves Convex Simple Bilevel Problem. - Huaqing Zhang 005; Lesi Chen; Jing Xu; Jingzhao Zhang (NeurIPS, 2024)

Online Policy Optimization for Robust Markov Decision Process. - Jing Dong 008; Jingwei Li; Baoxiang Wang 001; Jingzhao Zhang (UAI, 2024)

Towards Black-Box Membership Inference Attack for Diffusion Models. - Jingwei Li; Jing Dong; Tianxing He; Jingzhao Zhang (CoRR, 2024)

Functionally Constrained Algorithm Solves Convex Simple Bilevel Problems. - Huaqing Zhang 005; Lesi Chen; Jing Xu; Jingzhao Zhang (CoRR, 2024)

Time Series Prediction of Gas Emission in Coal Mining Face Based on Optimized Variational Mode Decomposition and SSA-LSTM. - Jingzhao Zhang; Yuxin Cui; Zhenguo Yan; Yuxin Huang 005; Chenyu Zhang; Jinlong Zhang; Jiantao Guo; Fei Zhao (Sensors, 2024)

Generalization Lower Bounds for GD and SGD in Smooth Stochastic Convex Optimization. - Peiyuan Zhang; Jiaye Teng; Jingzhao Zhang (AISTATS, 2025)

Solving Convex-Concave Problems with 풪(ε-4/7) Second-Order Oracle Complexity. - Lesi Chen; Chengchang Liu; Luo Luo; Jingzhao Zhang (COLT, 2025)

Fast and Multiphase Rates for Nearest Neighbor Classifiers. - Pengkun Yang; Jingzhao Zhang (COLT, 2025)

Second-Order Min-Max Optimization with Lazy Hessians. - Lesi Chen; Chengchang Liu; Jingzhao Zhang (ICLR, 2025)

From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency. - Kaiyue Wen; Huaqing Zhang 005; Hongzhou Lin; Jingzhao Zhang (ICLR, 2025)

Task Generalization With AutoRegressive Compositional Structure: Can Learning From D Tasks Generalize to DT Tasks? - Amirhesam Abedsoltan; Huaqing Zhang 005; Kaiyue Wen; Hongzhou Lin; Jingzhao Zhang; Mikhail Belkin (CoRR, 2025)

Scalable Model Merging with Progressive Layer-wise Distillation. - Jing Xu; Jiazheng Li 015; Jingzhao Zhang (CoRR, 2025)

Understanding Nonlinear Implicit Bias via Region Counts in Input Space. - Jingwei Li; Jing Xu; Zifan Wang; Huishuai Zhang; Jingzhao Zhang (CoRR, 2025)

Data Mixing Can Induce Phase Transitions in Knowledge Acquisition. - Xinran Gu; Kaifeng Lyu; Jiazheng Li 015; Jingzhao Zhang (CoRR, 2025)

Solving Convex-Concave Problems with 𝒪(ε-4/7) Second-Order Oracle Complexity. - Lesi Chen; Chengchang Liu; Luo Luo; Jingzhao Zhang (CoRR, 2025)

QuestA: Expanding Reasoning Capacity in LLMs via Question Augmentation. - Jiazheng Li; Hong Lu; Kaiyue Wen; Zaiwen Yang; Jiaxuan Gao; Hongzhou Lin; Yi Wu; Jingzhao Zhang (CoRR, 2025)

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database. - Weizhi Fei; Hao Shi; Jing Xu; Jingchen Peng; Jiazheng Li; Jingzhao Zhang; Bo Bai 001; Wei Han 004; Zhenyuan Chen; Xueyan Niu 001 (CoRR, 2025)