| Spiderboost and momentum: Faster variance reduction algorithms Z Wang, K Ji, Y Zhou, Y Liang, V Tarokh Advances in Neural Information Processing Systems 32, 2019 | 92 | 2019 |
| Spiderboost: A class of faster variance-reduced algorithms for nonconvex optimization Z Wang, K Ji, Y Zhou, Y Liang, V Tarokh arXiv 2018, 2018 | 74 | 2018 |
| Bilevel optimization: Convergence analysis and enhanced design K Ji, J Yang, Y Liang International Conference on Machine Learning, 4882-4892, 2021 | 56* | 2021 |
| Improved zeroth-order variance reduced algorithms and analysis for nonconvex optimization K Ji, Z Wang, Y Zhou, Y Liang International conference on machine learning, 3100-3109, 2019 | 42 | 2019 |
| Theoretical Convergence of Multi-Step Model-Agnostic Meta-Learning. K Ji, J Yang, Y Liang J. Mach. Learn. Res. 23, 29:1-29:41, 2022 | 41* | 2022 |
| Convergence of meta-learning with task-specific adaptation over partial parameters K Ji, JD Lee, Y Liang, HV Poor Advances in Neural Information Processing Systems 33, 11490-11500, 2020 | 25 | 2020 |
| Provably faster algorithms for bilevel optimization J Yang, K Ji, Y Liang Advances in Neural Information Processing Systems 34, 13670-13682, 2021 | 22 | 2021 |
| A new one-point residual-feedback oracle for black-box learning and control Y Zhang, Y Zhou, K Ji, MM Zavlanos Automatica 136, 110006, 2022 | 18* | 2022 |
| Lower bounds and accelerated algorithms for bilevel optimization K Ji, Y Liang arXiv preprint arXiv:2102.03926, 2021 | 18 | 2021 |
| When will gradient methods converge to max‐margin classifier under ReLU models? T Xu, Y Zhou, K Ji, Y Liang Stat 10 (1), e354, 2021 | 17* | 2021 |
| On resource pooling and separation for lru caching J Tan, G Quan, K Ji, N Shroff SIGMETRICS 2018 2 (1), 5, 2018 | 15 | 2018 |
| History-gradient aided batch size adaptation for variance reduced algorithms K Ji, Z Wang, B Weng, Y Zhou, W Zhang, Y Liang International Conference on Machine Learning, 4762-4772, 2020 | 14* | 2020 |
| Robust stochastic bandit algorithms under probabilistic unbounded adversarial attack Z Guan, K Ji, DJ Bucci Jr, TY Hu, J Palombo, M Liston, Y Liang Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 4036-4043, 2020 | 13 | 2020 |
| Momentum schemes with stochastic variance reduction for nonconvex composite optimization Y Zhou, Z Wang, K Ji, Y Liang, V Tarokh arXiv preprint arXiv:1902.02715, 2019 | 11 | 2019 |
| Minimax estimation of neural net distance K Ji, Y Liang Advances in Neural Information Processing Systems 31, 2018 | 9 | 2018 |
| Asymptotic miss ratio of LRU caching with consistent hashing K Ji, G Quan, J Tan IEEE INFOCOM 2018-IEEE Conference on Computer Communications, 450-458, 2018 | 8 | 2018 |
| Learning latent features with pairwise penalties in matrix completion K Ji, J Tan, Y Chi, J Xu IEEE Transactions on Signal Processing, 2020 | 7 | 2020 |
| LRU caching with dependent competing requests G Quan, K Ji, J Tan IEEE INFOCOM 2018-IEEE Conference on Computer Communications, 459-467, 2018 | 6 | 2018 |
| Boosting one-point derivative-free online optimization via residual feedback Y Zhang, Y Zhou, K Ji, MM Zavlanos arXiv preprint arXiv:2010.07378, 2020 | 5 | 2020 |
| Understanding estimation and generalization error of generative adversarial networks K Ji, Y Zhou, Y Liang IEEE Transactions on Information Theory 67 (5), 3114-3129, 2021 | 4 | 2021 |
Yingbin LiangThe Ohio State UniversityVerified email at osu.edu
