| MaxUp: Lightweight Adversarial Training with Data Augmentation Improves Neural Network Training C Gong, T Ren, M Ye, Q Liu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 124* | 2020 |
| Good subnetworks provably exist: Pruning via greedy forward selection M Ye, C Gong, L Nie, D Zhou, A Klivans, Q Liu International Conference on Machine Learning, 10820-10830, 2020 | 104 | 2020 |
| SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions M Ye, C Gong, Q Liu Proceedings of the 58th Annual Meeting of the Association for Computational …, 2020 | 90 | 2020 |
| Diffusion-based molecule generation with informative prior bridges L Wu, C Gong, X Liu, M Ye, Q Liu Advances in Neural Information Processing Systems 2022, 2022 | 65 | 2022 |
| Learning diffusion bridges on constrained domains X Liu, L Wu international conference on learning representations (ICLR), 2023 | 64* | 2023 |
| Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework D Zhang, M Ye, C Gong, Z Zhu, Q Liu Advances in Neural Information Processing Systems 33 (2020): 2316-2326., 2020 | 57 | 2020 |
| Vcnet and functional targeted regularization for learning causal effects of continuous treatments L Nie, M Ye, Q Liu, D Nicolae International Conference on Learning Representations 2021, 2021 | 49 | 2021 |
| Stein neural sampler T Hu, Z Chen, H Sun, J Bai, M Ye, G Cheng arXiv preprint arXiv:1810.03545, 2018 | 44 | 2018 |
| Bome! bilevel optimization made easy: A simple first-order approach M Ye, B Liu, S Wright, P Stone, Q Liu Advances in Neural Information Processing Systems 35 (2022): 17248-17262., 2022 | 43* | 2022 |
| Post-training quantization with multiple points: Mixed precision without mixed precision X Liu, M Ye, D Zhou, Q Liu Proceedings of the AAAI conference on artificial intelligence 35 (10), 8697-8705, 2021 | 38 | 2021 |
| Variable selection via penalized neural network: a drop-out-one loss approach M Ye, Y Sun In International Conference on Machine Learning, pp. 5620-5629. PMLR, 2018., 2018 | 31 | 2018 |
| Extended stochastic gradient Markov chain Monte Carlo for large-scale Bayesian variable selection Q Song, Y Sun, M Ye, F Liang Biometrika 107 (4), 997-1004, 2020 | 19 | 2020 |
| Go Wide, Then Narrow: Efficient Training of Deep Thin Networks D Zhou, M Ye, C Chen, T Meng, M Tan, X Song, Q Le, Q Liu, ... In International Conference on Machine Learning, pp. 11546-11555. PMLR, 2020., 2020 | 19 | 2020 |
| First Hitting Diffusion Models for Generating Manifold, Graph and Categorical Data M Ye, L Wu, Q Liu Advances in Neural Information Processing Systems. 2022., 2022 | 15 | 2022 |
| Greedy optimization provably wins the lottery: Logarithmic number of winning tickets is enough M Ye, L Wu, Q Liu Advances in Neural Information Processing Systems 33 (2020): 16409-16420., 2020 | 13 | 2020 |
| Steepest descent neural architecture optimization: Escaping local optimum with signed neural splitting L Wu, M Ye, Q Lei, JD Lee, Q Liu arXiv preprint arXiv:2003.10392, 2020 | 11 | 2020 |
| Stein Self-Repulsive Dynamics: Benefits From Past Samples M Ye, T Ren, Q Liu Advances in Neural Information Processing Systems 33, 2020 | 11 | 2020 |
| Clustering sparse binary data with hierarchical Bayesian Bernoulli mixture model M Ye, P Zhang, L Nie Computational Statistics & Data Analysis 123, 32-49, 2018 | 11 | 2018 |
| Finite mixture of varying coefficient model: Estimation and component selection M Ye, ZH Lu, Y Li, X Song Journal of Multivariate Analysis 171, 452-474, 2019 | 7 | 2019 |
| Adaptive dense-to-sparse paradigm for pruning online recommendation system with non-stationary data M Ye, D Choudhary, J Yu, E Wen, Z Chen, J Yang, J Park, Q Liu, ... arXiv preprint arXiv:2010.08655, 2020 | 6 | 2020 |
