Publications
Conference Papers
[21] Mao Ye, Lemeng Wu and Qiang Liu. First Hitting Diffusion Models. NeurIPS 2022
[20] Mao Ye * , Bo Liu * , Stephen Wright and Qiang Liu. BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach. NeurIPS 2022
[19] Lemeng Wu, Chengyue Gong, Xingchao Liu, Mao Ye and Qiang Liu. Diffusion-based Molecule Generation with Informative Prior Bridges. NeurIPS 2022
[18] Mao Ye and Qiang Liu. Centroid Approximation for Bootstrap: Improving Particle Quality at Inference. ICML 2022
[17] Mao Ye and Qiang Liu. Pareto Navigation Gradient Descent: a First-Order Algorithm for Optimization in Pareto Set. UAI 2022
[16] Mao Ye, Ruichen Jiang, Haoxiang Wang, Dhruv Choudhary, Xiaocong Du, Bhargav Bhushanam, Aryan Mokhtari, Arun Kejariwal and Qiang Liu. Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems. UAI 2022
[15] Mao Ye, Chenxi Liu, Maoqing Yao, Weiyue Wang, Zhaoqi Leng, Charles R. Qi and Dragomir Anguelov. Multi-Class 3D Object Detection with Single-Class Supervision. ICRA 2022
[14] Chengyue Gong, Mao Ye and Qiang Liu. Argmax Centroids: with Applications to Multi-domain Learning. NeurIPS 2021
[13] Chengyue Gong, Tongzheng Ren, Mao Ye and Qiang Liu. MaxUp: Lightweight Adversarial Training with Data Augmentation Improves Neural Network Training CVPR 2021
[12] Lizhen Nie * , Mao Ye * , Qiang Liu and Dan Nicolae. Varying Coefficient Neural Network with Functional Targeted Regularization for Estimating Continuous Treatment Effects. ICLR 2021 (Oral Presentation, accept rate 1.77%)
[11] Mao Ye * , Lemeng Wu * and Qiang Liu. Greedy Optimization Provably Wins the Lottery: Logarithmic Number of Winning Tickets is Enough. NeurIPS 2020
[10] Dinghuai Zhang * , Mao Ye * , Chengyue Gong * and Qiang Liu. Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework. NeurIPS 2020
[9] Mao Ye * , Tongzheng Ren * and Qiang Liu. Stein Self-Repulsive Dynamics: Benefits from Past Samples. NeurIPS 2020
[8] Xingchao Liu, Mao Ye, Denny Zhou and Qiang Liu. Post-training Quantization with Multiple Points: Mixed Precision without Mixed Precision. AAAI 2020
[7] Mao Ye, Chengyue Gong * , Lizhen Nie * , Denny Zhou, Adam Klivans and Qiang Liu. Good Subnetworks Provably Exists: Pruning via Greedy Forward Selection. ICML 2020
[6] Denny Zhou, Mao Ye, Chen Chen, Tianjian Meng, Mingxing Tan, Xiaodan Song, Quoc Le, Qiang Liu, Dale Schuurmans. Go Wide, Then Narrow: Efficient Training of Deep Thin Networks. ICML 2020
[5] Mao Ye * , Chengyue Gong * and Qiang Liu. SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions. ACL 2020
[4] Mao Ye * and Yan Sun * . Variable Selection via Penalized Neural Network: a Drop-Out-One Loss Approach. ICML 2018
Journal Papers
[3] Qifan Song, Yan Sun, Mao Ye and Faming Liang. (2020). Extended Stochastic Gradient MCMC for Large-Scale Bayesian Variable Selection. Biometrika
[2] Mao Ye, Zhaohua Lu, and Xingyuan Song. (2018). Finite mixture of varying coefficient model: estimation and component selection. Journal of Multivariate Analysis
[1] Mao Ye, Peng Zhang, and Lizhen Nie. (2017). Cluster analysis of ultra sparse binary data with a hierarchical Bayesian Bernoulli mixture model. Computational Statistics & Data Analysis