Mingyuan Zhou
Associate Professor
Department: Information, Risk & Operations Management
Research Areas: Artificial Intelligence, Bayesian Methods, Probability and Statistics, Statistical Analysis
Mingyuan Zhou is an associate professor at The University of Texas at Austin’s McCombs School of Business. He has taught courses on advanced data mining and web analytics, Bayesian deep learning, statistics and modeling, and Bayesian methods for machine learning.
Prior to joining McCombs, Zhou was a research assistant at Duke University. His interest in machine learning, Bayesian statistics, deep learning, and discrete data analysis has resulted in several well-respected papers. His most cited article is for the IEEE Transactions on Image Processing journal, which covers a nonparametric Bayesian method of recovering images based on compressive, incomplete, or noisy measurements. He has also presented his work on nonparametric Bayesian dictionary learning at the Neural Information Processing Systems Conference.
Zhou graduated from Nanjing University with a B.S. in acoustics and electrical engineering in 2005. He earned an M.Eng. in signal and information processing from the Chinese Academy of Sciences in 2008. In 2013, he earned a Ph.D. in Bayesian statistics and machine learning from Duke University.
ACADEMIC LEADERSHIP & AWARDS
2023 |
McCombs School of Business Research Excellence Award for Associate Professors |
2018 |
McCombs Research Excellence Grant CBA Foundations Research Excellence Award for Assistant Professors |
2015 |
Best Student Poster Award in NIPS 2015 Workshop: Networks in the Social and Information Sciences |
2012 |
NIPS travel award |
2009 |
NIPS travel award |
2008 |
ECE Ph.D. Student Fellowship, Duke University |
Publications
Tianqi Chen and Mingyuan Zhou. Learning to Jump: Thinning and Thickening Latent Counts as a General Method for Generative Modeling. International Conference on Machine Learning (4/24/2023).
Mingyuan Zhou, Korawat Tanwisuth, Shujian Zhang, Huangjie Zheng, Pengcheng He. POUF: Prompt-oriented Unsupervised Fine-tuning for Large Pre-trained Models. International Conference on Machine Learning (4/24/2023).
Yilin He, Chaojie Wang, Hao Zhang, Bo Chen, and Mingyuan Zhou. A Variational Edge Partition Model for Supervised Graph Representation Learning. Neural Information Processing Systems. Forthcoming.
Shentao Yang, Shujian Zhang, Yihao Feng, and Mingyuan Zhou, A Unified Framework for Alternating Offline Model Training and Policy Learning. Neural Information Processing Systems. Forthcoming.
Shentao Yang, Yihao Feng, Shujian Zhang, and Mingyuan Zhou. Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement Learning. International Conference on Machine Learning. Forthcoming.
Chaojie Wang, Bo Chen, Zhibin Duan, Wenchao Chen, Hao Zhang, and Mingyuan Zhou. Generative Text Convolutional Neural Network for Hierarchical Document Representation Learning. IEEE Transactions on Pattern Analysis & Machine Learning. Forthcoming.
Xizewen Han, Huangjie Zheng, and Mingyuan Zhou. CARD: Classification and Regression Diffusion Models. Neural Information Processing Systems. Forthcoming.
Dandan Guo, Ruiying Lu, Bo Chen, Zequn Zeng, and Mingyuan Zhou. Matching Visual Features to Hierarchical Semantic Topics for Image Paragraph Captioning. International Journal of Computer Vision. Forthcoming
Wenchao Chen, Bo Chen, Yicheng Liu, Chaojie Wang, Xiaojun Peng, Hongwei Liu, and Mingyuan Zhou. 2022. Infinite Switching Dynamic Probabilistic Network with Bayesian Nonparametric Learning. IEEE Transactions on Signal Processing 70:2224-2238.
Aaron Schein, Scott W. Linderman, Mingyuan Zhou, David M. Blei, and Hanna Wallach. Poisson Randomized Gamma Dynamical Systems. NeurIPS 2019 [Proceedings of the 32nd Advances in Neural Information Processing Systems, forthcoming.
Arman Hasanzadeh, Ehsan Hajiramezanali, Nick Duffield, Krishna R. Narayanan, Mingyuan Zhou, and Xiaoning Qian. Semi- Implicit Graph Variational Auto-Encoders. NeurIPS 2019 [Proceedings of the 32nd Advances in Neural Information Processing Systems] , forthcoming.
Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield, Krishna R. Narayanan, Mingyuan Zhou, and Xiaoning Qian. Variational Graph Recurrent Neural Networks. NeurIPS 2019 [Proceedings of the 32nd Advances in Neural Information Processing Systems] , forthcoming.
Hao Zhang, Bo Chen, Yulai Cong, Dandan Guo, Hongwei Liu, and Mingyuan Zhou. 2021. Deep Autoencoding Topic Model With Scalable Hybrid Bayesian Inference. IEEE Transactions on Pattern Analysis & Machine Intelligence 43(12), 4306-4322.
Mingyuan Zhou, Oscar Hernan Madrid, and James Scott. 2016. Priors for Random Count Matrices Derived from a Family of Negative Binomial Processes. Journal of the American Statistical Association 111(515), 1144-1156.
Gungor Polatkan, Mingyuan Zhou, Lawrence Carin, David Blei, and Ingrid Daubechies. 2015. A Bayesian Nonparametric Approach to Image Super-Resolution. IEEE Transactions on Pattern Analysis & Machine Intelligence 37(2), 346-358.
Mingyuan Zhou and Lawrence Carin. 2015. Negative Binomial Process Count and Mixture Modeling. IEEE Transactions on Pattern Analysis & Machine Intelligence 37(2), 307-320.
Mingyuan Zhou and L. Carin. 2012. Augment-and-Conquer Negative Binomial Processes. Neural Information Processing Systems, Dec.
Mingyuan Zhou, L. Hannah, D. Dunson, and L. Carin. 2012. Beta-Negative Binomial Process and Poisson Factor Analysis. Journal of Machine Learning Research, W&CP 22(Apr), 1462-1471.
Z. Xing, Mingyuan Zhou, A. Castrodad, G. Sapiro, and L. Carin. 2012. Dictionary Learning for Noisy and Incomplete Hyperspectral Images. SIAM Journal on Imaging Sciences 5(Jan), 35-56.
Mingyuan Zhou, L. Li, D. Dunson, and L. Carin. 2012. Lognormal and Gamma Mixed Negative Binomial Regression. International Conference on Machine Learning, Jun.
Mingyuan Zhou, H. Chen, J. Paisley, L. Ren, L. Li, Z. Xing, D. Dunson, G. Sapiro, and L. Carin. 2012. Nonparametric Bayesian Dictionary Learning for Analysis of Noisy and Incomplete Images. IEEE Trans. Image Processing 21(Jan), 130-144.
Mingyuan Zhou, H. Yang, G. Sapiro, D. Dunson, and L. Carin. 2011. Dependent Hierarchical Beta Process for Image Interpolation and Denoising. Journal of Machine Learning, W&CP 15(Apr), 883-891.