| Why gradient clipping accelerates training: A theoretical justification for adaptivity AJ Jingzhao Zhang, Tianxing He, Suvrit Sra International Conference on Learning Representations 2020, 2020 | 776* | 2020 |
| Why are adaptive methods good for attention models? J Zhang, SP Karimireddy, A Veit, S Kim, S Reddi, S Kumar, S Sra Advances in Neural Information Processing Systems 33, 15383-15393, 2020 | 479* | 2020 |
| 3D computer-generated holography by non-convex optimization J Zhang, N Pégard, J Zhong, H Adesnik, L Waller Optica 4 (10), 1306-1313, 2017 | 288 | 2017 |
| Complexity of finding stationary points of nonconvex nonsmooth functions J Zhang, H Lin, S Jegelka, S Sra, A Jadbabaie International Conference on Machine Learning, 11173-11182, 2020 | 157 | 2020 |
| Realistic fault detection of li-ion battery via dynamical deep learning J Zhang, Y Wang, B Jiang, H He, S Huang, C Wang, Y Zhang, X Han, ... Nature Communications 14 (1), 5940, 2023 | 141 | 2023 |
| Fast federated learning in the presence of arbitrary device unavailability X Gu, K Huang, J Zhang, L Huang Advances in Neural Information Processing Systems 34, 12052-12064, 2021 | 141 | 2021 |
| Direct Runge-Kutta Discretization Achieves Acceleration J Zhang, A Mokhtari, S Sra, A Jadbabaie Advances in Neural Information Processing Systems 2018, 2018 | 140 | 2018 |
| Understanding the unstable convergence of gradient descent K Ahn, J Zhang, S Sra International conference on machine learning, 247-257, 2022 | 107 | 2022 |
| Coping with label shift via distributionally robust optimisation J Zhang, A Menon, A Veit, S Bhojanapalli, S Kumar, S Sra ICLR 2021, 2020 | 100 | 2020 |
| Exposure bias versus self-recovery: Are distortions really incremental for autoregressive text generation? T He, J Zhang, Z Zhou, J Glass ACL 2021, 2019 | 88* | 2019 |
| On finding small hyper-gradients in bilevel optimization: Hardness results and improved analysis L Chen, J Xu, J Zhang The Thirty Seventh Annual Conference on Learning Theory, 947-980, 2024 | 65* | 2024 |
| Complexity lower bounds for nonconvex-strongly-concave min-max optimization H Li, Y Tian, J Zhang, A Jadbabaie Advances in Neural Information Processing Systems 34, 1792-1804, 2021 | 65 | 2021 |
| A probe towards understanding gan and vae models L Mi, M Shen, J Zhang arXiv preprint arXiv:1812.05676, 2018 | 47 | 2018 |
| R-SPIDER: A Fast Riemannian Stochastic Optimization Algorithm with Curvature Independent Rate J Zhang, H Zhang, S Sra arXiv preprint arXiv:1811.04194, 2018 | 45 | 2018 |
| Provably efficient algorithms for multi-objective competitive rl T Yu, Y Tian, J Zhang, S Sra International Conference on Machine Learning, 12167-12176, 2021 | 35 | 2021 |
| On the overlooked pitfalls of weight decay and how to mitigate them: A gradient-norm perspective Z Xie, Z Xu, J Zhang, I Sato, M Sugiyama Advances in Neural Information Processing Systems 36, 1208-1228, 2023 | 34 | 2023 |
| Sion’s minimax theorem in geodesic metric spaces and a Riemannian extragradient algorithm P Zhang, J Zhang, S Sra SIAM Journal on Optimization 33 (4), 2885-2908, 2023 | 33* | 2023 |
| Online policy optimization for robust mdp J Dong, J Li, B Wang, J Zhang UAI 2024; arXiv preprint arXiv:2209.13841, 2022 | 30 | 2022 |
| Efficient sampling on Riemannian manifolds via Langevin MCMC X Cheng, J Zhang, S Sra Advances in Neural Information Processing Systems 35, 5995-6006, 2022 | 24* | 2022 |
| Achieving Acceleration in Distributed Optimization via Direct Discretization of the Heavy-Ball ODE J Zhang, CA Uribe, A Mokhtari, A Jadbabaie 2019 American Control Conference, 2018 | 24 | 2018 |
Suvrit SraTUMVerified email at tum.de
