| A simple stochastic variance reduced algorithm with fast convergence rates K Zhou, F Shang, J Cheng International Conference on Machine Learning (ICML) 80, 5980-5989, 2018 | 101 | 2018 |
| VR-SGD: A simple stochastic variance reduction method for machine learning F Shang, K Zhou, H Liu, J Cheng, IW Tsang, L Zhang, D Tao, L Jiao IEEE Transactions on Knowledge and Data Engineering 32 (1), 188-202, 2018 | 78 | 2018 |
| Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization Y Chen, K Zhou, Y Bian, B Xie, B Wu, Y Zhang, MA KAILI, H Yang, P Zhao, ... International Conference on Learning Representations (ICLR), 2023 | 67 | 2023 |
| Hyper-sphere quantization: Communication-efficient SGD for federated learning X Dai, X Yan, K Zhou, H Yang, KKW Ng, J Cheng, Y Fan arXiv preprint arXiv:1911.04655, 2019 | 66 | 2019 |
| Direct acceleration of SAGA using sampled negative momentum K Zhou, Q Ding, F Shang, J Cheng, D Li, ZQ Luo International Conference on Artificial Intelligence and Statistics (AISTATS …, 2019 | 62 | 2019 |
| Does Invariant Graph Learning via Environment Augmentation Learn Invariance? Y Chen, Y Bian, K Zhou, B Xie, B Han, J Cheng Advances in Neural Information Processing Systems (NeurIPS), 2023 | 42 | 2023 |
| Understanding and Improving Feature Learning for Out-of-Distribution Generalization Y Chen, W Huang, K Zhou, Y Bian, B Han, J Cheng Advances in Neural Information Processing Systems (NeurIPS), 2023 | 41 | 2023 |
| On the Finite-Time Complexity and Practical Computation of Approximate Stationarity Concepts of Lipschitz Functions L Tian, K Zhou, AMC So International Conference on Machine Learning (ICML), 21360-21379, 2022 | 38 | 2022 |
| Asvrg: Accelerated proximal svrg F Shang, L Jiao, K Zhou, J Cheng, Y Ren, Y Jin Asian Conference on Machine Learning, 815-830, 2018 | 34 | 2018 |
| Convolutional embedding for edit distance X Dai, X Yan, K Zhou, Y Wang, H Yang, J Cheng International ACM SIGIR Conference on Research and Development in …, 2020 | 32* | 2020 |
| Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack R Gao, J Wang, K Zhou, F Liu, B Xie, G Niu, B Han, J Cheng International Conference on Machine Learning (ICML), 7144-7163, 2022 | 18 | 2022 |
| Practical Schemes for Finding Near-Stationary Points of Convex Finite-Sums K Zhou, L Tian, AMC So, J Cheng International Conference on Artificial Intelligence and Statistics (AISTATS …, 2021 | 14 | 2021 |
| Guaranteed sufficient decrease for stochastic variance reduced gradient optimization F Shang, Y Liu, K Zhou, J Cheng, KKW Ng, Y Yoshida International Conference on Artificial Intelligence and Statistics (AISTATS …, 2018 | 12 | 2018 |
| Amortized Nesterov’s Momentum: A Robust Momentum and Its Application to Deep Learning K Zhou, Y Jin, Q Ding, J Cheng Conference on Uncertainty in Artificial Intelligence (UAI) 124, 211-220, 2020 | 8 | 2020 |
| Boosting First-order Methods by Shifting Objective: New Schemes with Faster Worst Case Rates K Zhou, AMC So, J Cheng Advances in Neural Information Processing Systems (NeurIPS), 15405–15416, 2020 | 7 | 2020 |
| Restoreagent: Autonomous image restoration agent via multimodal large language models H Chen, W Li, J Gu, J Ren, S Chen, T Ye, R Pei, K Zhou, F Song, L Zhu arXiv preprint arXiv:2407.18035, 2024 | 5 | 2024 |
| Local reweighting for adversarial training R Gao, F Liu, K Zhou, G Niu, B Han, J Cheng arXiv preprint arXiv:2106.15776, 2021 | 5 | 2021 |
| Efficient private SCO for heavy-tailed data via averaged clipping C Jin, K Zhou, B Han, J Cheng, T Zeng Machine Learning 113 (11), 8487-8532, 2024 | 4* | 2024 |
| An adaptive incremental gradient method with support for non-euclidean norms B Xie, C Jin, K Zhou, J Cheng, W Meng arXiv preprint arXiv:2205.02273, 2022 | 4 | 2022 |
| Tight Convergence Rate of Gradient Descent for Eigenvalue Computation Q Ding, K Zhou, J Cheng International Joint Conference on Artificial Intelligence (IJCAI), 2020 | 4 | 2020 |
Binghui XieThe Chinese University of Hong KongVerified email at cse.cuhk.edu.hk
