Mingzhang Yin
- Assistant Professor
Location
- Warrington College of Business
- Marketing Department
- Stuzin Hall 260
Mingzhang Yin is an Assistant Professor of Marketing at the Warrington College of Business at the University of Florida. His primary research focuses on the areas of probabilistic machine learning, Bayesian methods, causal inference, with a focus on applications in online marketing, advertising, user generated content, and data-driven consumer analysis.
Expertise and interest areas
- Bayesian Methods
- Causal Inference
- Marketing Analytics
- Probabilistic machine learning
News
Meet Warrington’s new faculty for 2022 – September 2, 2022 – The Warrington College of Business is proud to welcome 11 new faculty members to campus in the 2022-2023 academic year. Learn more about these outstanding…
Courses taught
- Individual Work (MAR6905)
- Marketing Analytics 2 (MAR6669)
- Supervised Research (MAR6910)
Education
- PhD – Statistics, The University of Texas at Austin, 2020
- BSc – Mathematics and Applied Mathematics, Fudan University, 2015
PhD students advised
- Hye Rin Kim — 2024 — 2025
- Zhiyu Zhang — 2024 — 2025
Professional service
- Associate Editor, International Conference on Artificial Intelligence and Statistics — 2024 to 2025
- Committee Member, Israel Science Foundation — 2025
- Book/Textbook Reviewer/Referee, Conference on Neural Information Processing Systems — 2024
- Book/Textbook Reviewer/Referee, Journal of Machine Learning Research — 2023 to 2024
- Book/Textbook Reviewer/Referee, Journal of the American Statistical Association — 2024
- Book/Textbook Reviewer/Referee, Production and Operations Management — 2024
- Associate Editor, International Conference on Artificial Intelligence and Statistics — 2023
- Book/Textbook Reviewer/Referee, Annals of Applied Statistics — 2023
- Book/Textbook Reviewer/Referee, Conference on Neural Information Processing Systems — 2023
- Session Chair, International Chinese Statistical Association — 2023
Conference proceeding publications
- Confounding-Robust Deferral Policy Learning
- Status: Published
- Published Year: 2025
- Authors: Ruijiang Gao, Mingzhang Yin
- SEL-BALD: Deep Bayesian Active Learning with Selective Labels
- Status: Published
- Published Year: 2024
- Authors: Ruijiang Gao, Mingzhang Yin, Maytal Saar-Tsechansky
- Score identity Distillation: Exponentially Fast Distillation of Pretrained Diffusion Models for One-Step Generation
- Status: Published
- Published Year: 2024
- Authors: Mingyuan Zhou, Huangjie Zheng, Mingzhang Yin, Zhendong Wang, Hai Huang
- Probabilistic Conformal Prediction Using Conditional Random Samples
- Status: Published
- Published Year: 2023
- Authors: Mingzhang Yin, Zhendong Wang, Ruijiang Gao, David Blei, Mingyuan Zhou
- Partial Identification with Noisy Covariates: A Robust Optimization Approach
- Status: Published
- Published Year: 2022
- Author: Mingzhang Yin
- A Theoretical Case Study of Structured Variational Inference for Community Detection
- Status: Published
- Published Year: 2020
- Author: Mingzhang Yin
- Discrete Action On-Policy Learning with Action-Value Critic
- Status: Published
- Published Year: 2020
- Author: Mingzhang Yin
- Meta-Learning without Memorization
- Status: Published
- Published Year: 2020
- Author: Mingzhang Yin
- Pairwise Supervised Hashing with Bernoulli Variational Auto-Encoder and Self-Control Gradient Estimator
- Status: Published
- Published Year: 2020
- Author: Mingzhang Yin
Journal article publications
- Unraveling Multifaceted User Preferences on Content Platforms: A Bayesian Deep Learning Approach
- Status: 1st Revise and Resubmit
- Authors: Mingzhang Yin, Ziwei Cong, Jia Liu
- Probabilistic Machine Learning: New Frontiers for Modeling Consumers and their Choice
- Status: Published
- Journal: International Journal of Research in Marketing
- Published Year: 2024
- Authors: Ryan Dew, Mingzhang Yin
- Adjusting Regression Models for Conditional Uncertainty Calibration
- Status: Published
- Journal: Machine Learning
- Published Year: 2024
- Authors: Ruijiang Gao, Mingzhang Yin, James Mcinerney, Nathan Kallus
- Optimization-based Causal Estimation from Heterogenous Environments
- Status: Published
- Journal: Journal of Machine Learning Research
- Accepted Year: 2024
- Authors: Mingzhang Yin, Yixin Wang, David Blei
- Nonparametric Discrete Choice Experiments with Machine Learning Guided Adaptive Design
- Status: Accepted
- Published Year: 2023
- Authors: Mingzhang Yin, Ruijiang Gao, Weiran Lin, Steven Shugan
- Gradient Estimation for Binary Latent Variables via Gradient Variance Clipping
- Status: Published
- Journal: Proceedings of the AAAI Conference on Artificial Intelligence
- Published Year: 2023
- Authors: Russell Kunes, Mingzhang Yin, Max Land, Doron Haviv, Dana Pe’er, Simon Tavare
- Conformal Sensitivity Analysis for Individual Treatment Effects
- Status: Published
- Journal: Journal of the American Statistical Association
- Published Year: 2022
- Authors: Mingzhang Yin, Yixin Wang, David Blei