Convergence of Gradient EM on Multi-component Mixture of Gaussians. - Bowei Yan; Mingzhang Yin; Purnamrita Sarkar (NIPS, 2017)

Statistical Convergence Analysis of Gradient EM on General Gaussian Mixture Models. - Bowei Yan; Mingzhang Yin; Purnamrita Sarkar (CoRR, 2017)

Semi-Implicit Variational Inference. - Mingzhang Yin; Mingyuan Zhou (ICML, 2018)

ARM: Augment-REINFORCE-Merge Gradient for Discrete Latent Variable Models. - Mingzhang Yin; Mingyuan Zhou (CoRR, 2018)

ARM: Augment-REINFORCE-Merge Gradient for Stochastic Binary Networks. - Mingzhang Yin; Mingyuan Zhou (ICLR (Poster), 2019)

ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical Variables. - Mingzhang Yin; Yuguang Yue; Mingyuan Zhou (ICML, 2019)

Augment-Reinforce-Merge Policy Gradient for Binary Stochastic Policy. - Yunhao Tang; Mingzhang Yin; Mingyuan Zhou (CoRR, 2019)

Semi-Implicit Generative Model. - Mingzhang Yin; Mingyuan Zhou (CoRR, 2019)

A Theoretical Case Study of Structured Variational Inference for Community Detection. - Mingzhang Yin; Y. X. Rachel Wang; Purnamrita Sarkar (AISTATS, 2020)

Discrete Action On-Policy Learning with Action-Value Critic. - Yuguang Yue; Yunhao Tang; Mingzhang Yin; Mingyuan Zhou (AISTATS, 2020)

Meta-Learning without Memorization. - Mingzhang Yin; George Tucker; Mingyuan Zhou; Sergey Levine; Chelsea Finn (ICLR, 2020)

Pairwise Supervised Hashing with Bernoulli Variational Auto-Encoder and Self-Control Gradient Estimator. - Siamak Zamani Dadaneh; Shahin Boluki; Mingzhang Yin; Mingyuan Zhou; Xiaoning Qian (UAI, 2020)

Optimization-based Causal Estimation from Heterogenous Environments. - Mingzhang Yin; Yixin Wang; David M. Blei (CoRR, 2021)

Partial Identification with Noisy Covariates: A Robust Optimization Approach. - Wenshuo Guo; Mingzhang Yin; Yixin Wang; Michael I. Jordan (CLeaR, 2022)

Gradient Estimation for Binary Latent Variables via Gradient Variance Clipping. - Russell Z. Kunes; Mingzhang Yin; Max Land; Doron Haviv; Dana Pe'er; Simon Tavaré (AAAI, 2023)

Probabilistic Conformal Prediction Using Conditional Random Samples. - Zhendong Wang; Ruijiang Gao; Mingzhang Yin; Mingyuan Zhou; David M. Blei (AISTATS, 2023)

Confounding-Robust Policy Improvement with Human-AI Teams. - Ruijiang Gao; Mingzhang Yin (CoRR, 2023)

Nonparametric Discrete Choice Experiments with Machine Learning Guided Adaptive Design. - Mingzhang Yin; Ruijiang Gao; Weiran Lin; Steven M. Shugan (CoRR, 2023)

Score identity Distillation: Exponentially Fast Distillation of Pretrained Diffusion Models for One-Step Generation. - Mingyuan Zhou; Huangjie Zheng; Zhendong Wang; Mingzhang Yin; Hai Huang (ICML, 2024)

SEL-BALD: Deep Bayesian Active Learning with Selective Labels. - Ruijiang Gao; Mingzhang Yin; Maytal Saar-Tsechansky (NeurIPS, 2024)

Ordinal Preference Optimization: Aligning Human Preferences via NDCG. - Yang Zhao; Yixin Wang; Mingzhang Yin (CoRR, 2024)

Optimization-based Causal Estimation from Heterogeneous Environments. - Mingzhang Yin; Yixin Wang; David M. Blei (J. Mach. Learn. Res., 2024)

Adjusting regression models for conditional uncertainty calibration. - Ruijiang Gao; Mingzhang Yin; James McInerney; Nathan Kallus (Mach. Learn., 2024)

Confounding-Robust Deferral Policy Learning. - Ruijiang Gao; Mingzhang Yin (AAAI, 2025)

Bayesian Invariance Modeling of Multi-Environment Data. - Luhuan Wu; Mingzhang Yin; Yixin Wang; John P. Cunningham; David M. Blei (CoRR, 2025)