| Milenas: Efficient neural architecture search via mixed-level reformulation C He, H Ye, L Shen, T Zhang Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 164 | 2020 |
| Stochastic recursive gradient descent ascent for stochastic nonconvex-strongly-concave minimax problems L Luo, H Ye, Z Huang, T Zhang Advances in Neural Information Processing Systems 33, 20566-20577, 2020 | 133 | 2020 |
| Multi-consensus decentralized accelerated gradient descent H Ye, L Luo, Z Zhou, T Zhang Journal of Machine Learning Research 24 (306), 1-50, 2023 | 66 | 2023 |
| Approximate newton methods H Ye, L Luo, Z Zhang Journal of Machine Learning Research 22 (66), 1-41, 2021 | 48* | 2021 |
| Hessian-aware zeroth-order optimization for black-box adversarial attack H Ye, Z Huang, C Fang, CJ Li, T Zhang arXiv preprint arXiv:1812.11377, 2018 | 47 | 2018 |
| Fast Fisher discriminant analysis with randomized algorithms H Ye, Y Li, C Chen, Z Zhang Pattern Recognition 72, 82-92, 2017 | 40 | 2017 |
| Decentralized accelerated proximal gradient descent H Ye, Z Zhou, L Luo, T Zhang Advances in Neural Information Processing Systems 33, 18308-18317, 2020 | 35 | 2020 |
| DeEPCA: Decentralized exact PCA with linear convergence rate H Ye, T Zhang Journal of Machine Learning Research 22 (238), 1-27, 2021 | 30 | 2021 |
| Nesterov's acceleration for approximate Newton H Ye, L Luo, Z Zhang Journal of Machine Learning Research 21 (142), 1-37, 2020 | 22* | 2020 |
| Towards explicit superlinear convergence rate for SR1 H Ye, D Lin, X Chang, Z Zhang Mathematical Programming 199 (1), 1273-1303, 2023 | 21* | 2023 |
| Explicit convergence rates of greedy and random quasi-Newton methods D Lin, H Ye, Z Zhang Journal of Machine Learning Research 23 (162), 1-40, 2022 | 20 | 2022 |
| Greedy and random quasi-newton methods with faster explicit superlinear convergence D Lin, H Ye, Z Zhang Advances in Neural Information Processing Systems 34, 6646-6657, 2021 | 19 | 2021 |
| PMGT-VR: A decentralized proximal-gradient algorithmic framework with variance reduction H Ye, W Xiong, T Zhang arXiv preprint arXiv:2012.15010, 2020 | 17 | 2020 |
| Explicit superlinear convergence rates of Broyden's methods in nonlinear equations D Lin, H Ye, Z Zhang arXiv preprint arXiv:2109.01974, 2021 | 14 | 2021 |
| Stochastic distributed optimization under average second-order similarity: Algorithms and analysis D Lin, Y Han, H Ye, Z Zhang Advances in Neural Information Processing Systems 36, 2024 | 13 | 2024 |
| Eigencurve: Optimal learning rate schedule for sgd on quadratic objectives with skewed hessian spectrums R Pan, H Ye, T Zhang arXiv preprint arXiv:2110.14109, 2021 | 12 | 2021 |
| Second-order fine-tuning without pain for llms: A hessian informed zeroth-order optimizer Y Zhao, S Dang, H Ye, G Dai, Y Qian, IW Tsang arXiv preprint arXiv:2402.15173, 2024 | 10 | 2024 |
| Decentralized Riemannian conjugate gradient method on the Stiefel manifold J Chen, H Ye, M Wang, T Huang, G Dai, IW Tsang, Y Liu arXiv preprint arXiv:2308.10547, 2023 | 10 | 2023 |
| Greedy and random Broyden's methods with explicit superlinear convergence rates in nonlinear equations H Ye, D Lin, Z Zhang arXiv preprint arXiv:2110.08572, 2021 | 10 | 2021 |
| An optimal stochastic algorithm for decentralized nonconvex finite-sum optimization L Luo, H Ye arXiv preprint arXiv:2210.13931, 2022 | 9 | 2022 |
