Mingzhang Yin

University of Florida
Warrington College of Business
Marketing Department
Stuzin Hall 260

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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. 

• PhD - Statistics, The University of Texas at Austin, 2020
• BSc - Mathematics and Applied Mathematics, Fudan University, 2015

• 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 the...

• Individual Work (MAR6905)
• Marketing Analytics 2 (MAR6669)
• Supervised Research (MAR6910)

• Hye Rin Kim — 2024 — 2025
• Zhiyu Zhang — 2024 — 2025
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• Associate Editor, International Conference on Artificial Intelligence and Statistics — 2024 to 2025
• Editorial Review Board Member, Israel Science Foundation — 2025
• Book/Textbook Reviewer/Referee, Journal of Machine Learning Research — 2023 to 2024
• Editorial Review Board Member, Advances in Neural Information Processing Systems — 2024
• Editorial Review Board Member, Journal of the American Statistical Association — 2024
• Editorial Review Board Member, 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
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• 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
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• 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
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