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Mao Ye
Mao Ye
Verified email at cs.utexas.edu - Homepage
Title
Cited by
Cited by
Year
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
125*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
1112020
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
992020
Learning diffusion bridges on constrained domains
X Liu, L Wu
international conference on learning representations (ICLR), 2023
78*2023
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
752022
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
682020
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
592021
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
53*2022
Stein neural sampler
T Hu, Z Chen, H Sun, J Bai, M Ye, G Cheng
arXiv preprint arXiv:1810.03545, 2018
482018
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
392021
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
322018
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
202020
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
202020
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
192022
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
142020
Stein Self-Repulsive Dynamics: Benefits From Past Samples
M Ye, T Ren, Q Liu
Advances in Neural Information Processing Systems 33, 2020
122020
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
112020
Clustering sparse binary data with hierarchical Bayesian Bernoulli mixture model
M Ye, P Zhang, L Nie
Computational Statistics & Data Analysis 123, 32-49, 2018
112018
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
82019
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
72020
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Articles 1–20