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 of University of Florida. His primary research interests are in the intersection of quantitative marketing, Bayesian statistics, probabilistic machine learning, and causal inference. He develops methodologies for probabilistic models, approximate Bayesian inference, and observational studies. He explores marketing applications in online advertisement, personalization, E-commerce 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...

• Marketing Analytics 2 (MAR6669)
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• Associate Editor, Artificial Intelligence and Statistics Conference — 2023 to 2024
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• Book/Textbook Reviewer/Referee, Journal of Machine Learning Research — 2023 to 2024
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• Book/Textbook Reviewer/Referee, Annals of Applied Statistics — 2023
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• Book/Textbook Reviewer/Referee, Conference on Neural Information Processing Systems — 2023
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• Session Chair, International Chinese Statistical Association — 2023
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• Probabilistic Conformal Prediction Using Conditional Random Samples
  Status: Published
  Published Year: 2023
  Authors: Mingzhang Yin, Zhendong Wang, Ruijiang Gao, David Blei, Mingyuan Zhou
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• Partial Identification with Noisy Covariates: A Robust Optimization Approach
  Status: Published
  Published Year: 2022
  Author: Mingzhang Yin
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• A Theoretical Case Study of Structured Variational Inference for Community Detection
  Status: Published
  Published Year: 2020
  Author: Mingzhang Yin
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• Discrete Action On-Policy Learning with Action-Value Critic
  Status: Published
  Published Year: 2020
  Author: Mingzhang Yin
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• Meta-Learning without Memorization
  Status: Published
  Published Year: 2020
  Author: Mingzhang Yin
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• 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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• ARSM: AugmentREINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical Variables
  Status: Published
  Published Year: 2019
  Author: Mingzhang Yin
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• ARM: Augment-REINFORCE-Merge Gradient for Stochastic Binary Networks
  Status: Published
  Published Year: 2019
  Author: Mingzhang Yin
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• Optimization-based Causal Estimation from Heterogenous Environments
  Status: Accepted
  Accepted Year: 2024
  Authors: Mingzhang Yin, Yixin Wang, David Blei
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• Unraveling Multifaceted User Preferences on Content Platforms: A Bayesian Deep Learning Approach
  Status: Working Paper
  Authors: Mingzhang Yin, Ziwei Cong, Jia Liu
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• Nonparametric Discrete Choice Experiments with Machine Learning Guided Adaptive Design
  Status: Accepted
  Published Year: 2023
  Authors: Mingzhang Yin, Ruijiang Gao, Weiran Lin, Steven Shugan
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• 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
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• 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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