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ORCIDJingzhao Zhang
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2020 – today
- 2024
[j3]Jingzhao Zhang, Yuxin Cui, Zhenguo Yan
, Yuxin Huang, Chenyu Zhang, Jinlong Zhang, Jiantao Guo, Fei Zhao:
Time Series Prediction of Gas Emission in Coal Mining Face Based on Optimized Variational Mode Decomposition and SSA-LSTM. Sensors 24(19): 6454 (2024)- [c22]Lesi Chen, Jing Xu, Jingzhao Zhang:
On Finding Small Hyper-Gradients in Bilevel Optimization: Hardness Results and Improved Analysis. COLT 2024: 947-980 - [c21]Xinran Gu, Kaifeng Lyu, Sanjeev Arora, Jingzhao Zhang, Longbo Huang:
A Quadratic Synchronization Rule for Distributed Deep Learning. ICLR 2024 - [c20]Jing Xu, Jingzhao Zhang:
Random Masking Finds Winning Tickets for Parameter Efficient Fine-tuning. ICML 2024 - [i32]Xiang Cheng, Jingzhao Zhang, Suvrit Sra:
Efficient Sampling on Riemannian Manifolds via Langevin MCMC. CoRR abs/2402.10357 (2024) - [i31]Jing Xu, Jingzhao Zhang:
Random Masking Finds Winning Tickets for Parameter Efficient Fine-tuning. CoRR abs/2405.02596 (2024) - [i30]Jingwei Li, Jing Dong, Tianxing He, Jingzhao Zhang:
Towards Black-Box Membership Inference Attack for Diffusion Models. CoRR abs/2405.20771 (2024) - [i29]Huaqing Zhang, Lesi Chen, Jing Xu, Jingzhao Zhang:
Functionally Constrained Algorithm Solves Convex Simple Bilevel Problems. CoRR abs/2409.06530 (2024) - [i28]Kaiyue Wen, Huaqing Zhang, Hongzhou Lin, Jingzhao Zhang:
From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency. CoRR abs/2410.05459 (2024) - [i27]Lesi Chen, Chengchang Liu, Jingzhao Zhang:
Second-Order Min-Max Optimization with Lazy Hessians. CoRR abs/2410.09568 (2024) - 2023
- [j2]Peiyuan Zhang, Jingzhao Zhang, Suvrit Sra:
Sion's Minimax Theorem in Geodesic Metric Spaces and a Riemannian Extragradient Algorithm. SIAM J. Optim. 33(4): 2885-2908 (2023) - [c19]Kaiyue Wen, Jiaye Teng, Jingzhao Zhang:
Benign Overfitting in Classification: Provably Counter Label Noise with Larger Models. ICLR 2023 - [c18]Xiang Cheng, Bohan Wang, Jingzhao Zhang, Yusong Zhu:
Fast Conditional Mixing of MCMC Algorithms for Non-log-concave Distributions. NeurIPS 2023 - [c17]Wei Fu, Weihua Du, Jingwei Li, Sunli Chen, Jingzhao Zhang, Yi Wu:
Iteratively Learn Diverse Strategies with State Distance Information. NeurIPS 2023 - [c16]Zeke Xie, Zhiqiang Xu, Jingzhao Zhang, Issei Sato, Masashi Sugiyama:
On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective. NeurIPS 2023 - [i26]Lesi Chen, Jing Xu, Jingzhao Zhang:
On Bilevel Optimization without Lower-level Strong Convexity. CoRR abs/2301.00712 (2023) - [i25]Peiyuan Zhang, Jiaye Teng, Jingzhao Zhang:
Lower Generalization Bounds for GD and SGD in Smooth Stochastic Convex Optimization. CoRR abs/2303.10758 (2023) - [i24]Jingwei Li, Jing Dong, Baoxiang Wang, Jingzhao Zhang:
Online Control with Adversarial Disturbance for Continuous-time Linear Systems. CoRR abs/2306.01952 (2023) - [i23]Xiang Cheng, Bohan Wang, Jingzhao Zhang, Yusong Zhu:
Fast Conditional Mixing of MCMC Algorithms for Non-log-concave Distributions. CoRR abs/2306.10506 (2023) - [i22]Lesi Chen, Yaohua Ma, Jingzhao Zhang:
Near-Optimal Fully First-Order Algorithms for Finding Stationary Points in Bilevel Optimization. CoRR abs/2306.14853 (2023) - [i21]Pengkun Yang, Jingzhao Zhang:
Two Phases of Scaling Laws for Nearest Neighbor Classifiers. CoRR abs/2308.08247 (2023) - [i20]Xinran Gu, Kaifeng Lyu, Sanjeev Arora, Jingzhao Zhang, Longbo Huang:
A Quadratic Synchronization Rule for Distributed Deep Learning. CoRR abs/2310.14423 (2023) - [i19]Wei Fu, Weihua Du, Jingwei Li, Sunli Chen, Jingzhao Zhang, Yi Wu:
Iteratively Learn Diverse Strategies with State Distance Information. CoRR abs/2310.14509 (2023) - 2022
- [b1]Jingzhao Zhang:
Optimization Theory and Machine Learning Practice: Mind the Gap. Massachusetts Institute of Technology, USA, 2022 - [c15]Kwangjun Ahn, Jingzhao Zhang, Suvrit Sra:
Understanding the unstable convergence of gradient descent. ICML 2022: 247-257 - [c14]Jingzhao Zhang, Haochuan Li, Suvrit Sra, Ali Jadbabaie:
Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective. ICML 2022: 26330-26346 - [c13]Jingzhao Zhang, Hongzhou Lin, Subhro Das, Suvrit Sra, Ali Jadbabaie:
Beyond Worst-Case Analysis in Stochastic Approximation: Moment Estimation Improves Instance Complexity. ICML 2022: 26347-26361 - [c12]Xiang Cheng, Jingzhao Zhang, Suvrit Sra:
Efficient Sampling on Riemannian Manifolds via Langevin MCMC. NeurIPS 2022 - [i18]Haowei He, Jingzhao Zhang, Yanan Wang, Shaobo Huang, Chen Wang, Yang Zhang, Dongxu Guo, Guannan He, Minggao Ouyang:
Detecting Electric Vehicle Battery Failure via Dynamic-VAE. CoRR abs/2201.12358 (2022) - [i17]Peiyuan Zhang, Jingzhao Zhang, Suvrit Sra:
Minimax in Geodesic Metric Spaces: Sion's Theorem and Algorithms. CoRR abs/2202.06950 (2022) - [i16]Kwangjun Ahn, Jingzhao Zhang, Suvrit Sra:
Understanding the unstable convergence of gradient descent. CoRR abs/2204.01050 (2022) - [i15]Kaiyue Wen, Jiaye Teng, Jingzhao Zhang:
Realistic Deep Learning May Not Fit Benignly. CoRR abs/2206.00501 (2022) - [i14]Jing Dong, Jingwei Li, Baoxiang Wang, Jingzhao Zhang:
Online Policy Optimization for Robust MDP. CoRR abs/2209.13841 (2022) - 2021
- [j1]Zhiyong Wang, Jingzhao Zhang, Yaran Yu, Jian Liu, Wei Liu, Na Jiang, Donge Guo:
Monitoring, Analyzing, and Modeling for Single Subsidence Basin in Coal Mining Areas Based on SAR Interferometry with L-Band Data. Sci. Program. 2021: 6662097:1-6662097:10 (2021) - [c11]Tianxing He, Jingzhao Zhang, Zhiming Zhou, James R. Glass:
Exposure Bias versus Self-Recovery: Are Distortions Really Incremental for Autoregressive Text Generation? EMNLP (1) 2021: 5087-5102 - [c10]Jingzhao Zhang, Aditya Krishna Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, Suvrit Sra:
Coping with Label Shift via Distributionally Robust Optimisation. ICLR 2021 - [c9]Tiancheng Yu, Yi Tian, Jingzhao Zhang, Suvrit Sra:
Provably Efficient Algorithms for Multi-Objective Competitive RL. ICML 2021: 12167-12176 - [c8]Haochuan Li, Yi Tian, Jingzhao Zhang, Ali Jadbabaie:
Complexity Lower Bounds for Nonconvex-Strongly-Concave Min-Max Optimization. NeurIPS 2021: 1792-1804 - [c7]Xinran Gu, Kaixuan Huang, Jingzhao Zhang, Longbo Huang:
Fast Federated Learning in the Presence of Arbitrary Device Unavailability. NeurIPS 2021: 12052-12064 - [i13]Tiancheng Yu, Yi Tian, Jingzhao Zhang, Suvrit Sra:
Provably Efficient Algorithms for Multi-Objective Competitive RL. CoRR abs/2102.03192 (2021) - [i12]Haochuan Li, Yi Tian, Jingzhao Zhang, Ali Jadbabaie:
Complexity Lower Bounds for Nonconvex-Strongly-Concave Min-Max Optimization. CoRR abs/2104.08708 (2021) - [i11]Xinran Gu, Kaixuan Huang, Jingzhao Zhang, Longbo Huang:
Fast Federated Learning in the Presence of Arbitrary Device Unavailability. CoRR abs/2106.04159 (2021) - [i10]Jingzhao Zhang, Haochuan Li, Suvrit Sra, Ali Jadbabaie:
On Convergence of Training Loss Without Reaching Stationary Points. CoRR abs/2110.06256 (2021) - 2020
- [c6]Jingzhao Zhang, Tianxing He, Suvrit Sra, Ali Jadbabaie:
Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity. ICLR 2020 - [c5]Jingzhao Zhang, Hongzhou Lin, Stefanie Jegelka, Suvrit Sra, Ali Jadbabaie:
Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions. ICML 2020: 11173-11182 - [c4]Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar, Suvrit Sra:
Why are Adaptive Methods Good for Attention Models? NeurIPS 2020 - [i9]Jingzhao Zhang, Hongzhou Lin, Suvrit Sra, Ali Jadbabaie:
On Complexity of Finding Stationary Points of Nonsmooth Nonconvex Functions. CoRR abs/2002.04130 (2020) - [i8]Jingzhao Zhang, Hongzhou Lin, Subhro Das, Suvrit Sra, Ali Jadbabaie:
Stochastic Optimization with Non-stationary Noise. CoRR abs/2006.04429 (2020) - [i7]Jingzhao Zhang, Aditya Krishna Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, Suvrit Sra:
Coping with Label Shift via Distributionally Robust Optimisation. CoRR abs/2010.12230 (2020)
2010 – 2019
- 2019
- [c3]Jingzhao Zhang, César A. Uribe, Aryan Mokhtari, Ali Jadbabaie:
Achieving Acceleration in Distributed Optimization via Direct Discretization of the Heavy-Ball ODE. ACC 2019: 3408-3413 - [c2]Jingzhao Zhang, Suvrit Sra, Ali Jadbabaie:
Acceleration in First Order Quasi-strongly Convex Optimization by ODE Discretization. CDC 2019: 1501-1506 - [i6]Tianxing He, Jingzhao Zhang, Zhiming Zhou, James R. Glass:
Quantifying Exposure Bias for Neural Language Generation. CoRR abs/1905.10617 (2019) - [i5]Jingzhao Zhang, Tianxing He, Suvrit Sra, Ali Jadbabaie:
Analysis of Gradient Clipping and Adaptive Scaling with a Relaxed Smoothness Condition. CoRR abs/1905.11881 (2019) - [i4]Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar, Suvrit Sra:
Why ADAM Beats SGD for Attention Models. CoRR abs/1912.03194 (2019) - 2018
- [c1]Jingzhao Zhang, Aryan Mokhtari, Suvrit Sra, Ali Jadbabaie:
Direct Runge-Kutta Discretization Achieves Acceleration. NeurIPS 2018: 3904-3913 - [i3]Jingzhao Zhang, Aryan Mokhtari, Suvrit Sra, Ali Jadbabaie:
Direct Runge-Kutta Discretization Achieves Acceleration. CoRR abs/1805.00521 (2018) - [i2]Jingzhao Zhang, Hongyi Zhang, Suvrit Sra:
R-SPIDER: A Fast Riemannian Stochastic Optimization Algorithm with Curvature Independent Rate. CoRR abs/1811.04194 (2018) - [i1]Lu Mi, Macheng Shen, Jingzhao Zhang:
A Probe Towards Understanding GAN and VAE Models. CoRR abs/1812.05676 (2018)
Coauthor Index
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