72 citations · 173 across the 16 of their papers we have counts for
16 papers
Score identity Distillation: Exponentially Fast Distillation of Pretrained Diffusion Models for One-Step Generation
Mingyuan Zhou, Huangjie Zheng, Zhendong Wang +2
We introduce Score identity Distillation (SiD), an innovative data-free method that distills the generative capabilities of pretrained diffusion models into a single-step generator…
Gradient Estimation for Binary Latent Variables via Gradient Variance Clipping
Russell Z. Kunes, Mingzhang Yin, Max Land +3
Gradient estimation is often necessary for fitting generative models with discrete latent variables, in contexts such as reinforcement learning and variational autoencoder (VAE) tr…
Probabilistic Conformal Prediction Using Conditional Random Samples
Zhendong Wang, Ruijiang Gao, Mingzhang Yin +2
This paper proposes probabilistic conformal prediction (PCP), a predictive inference algorithm that estimates a target variable by a discontinuous predictive set. Given inputs, PCP…
Partial Identification with Noisy Covariates: A Robust Optimization Approach
Wenshuo Guo, Mingzhang Yin, Yixin Wang +1
Causal inference from observational datasets often relies on measuring and adjusting for covariates. In practice, measurements of the covariates can often be noisy and/or biased, o…
Conformal Sensitivity Analysis for Individual Treatment Effects
Mingzhang Yin, Claudia Shi, Yixin Wang +1
Estimating an individual treatment effect (ITE) is essential to personalized decision making. However, existing methods for estimating the ITE often rely on unconfoundedness, an as…
Optimization-based Causal Estimation from Heterogenous Environments
Mingzhang Yin, Yixin Wang, David M. Blei
This paper presents a new optimization approach to causal estimation. Given data that contains covariates and an outcome, which covariates are causes of the outcome, and what is th…
Probabilistic Best Subset Selection via Gradient-Based Optimization
Mingzhang Yin, Nhat Ho, Bowei Yan +2
In high-dimensional statistics, variable selection recovers the latent sparse patterns from all possible covariate combinations. This paper proposes a novel optimization method to…
Pairwise Supervised Hashing with Bernoulli Variational Auto-Encoder and Self-Control Gradient Estimator
Siamak Zamani Dadaneh, Shahin Boluki, Mingzhang Yin +2
Semantic hashing has become a crucial component of fast similarity search in many large-scale information retrieval systems, in particular, for text data. Variational auto-encoders…
Discrete Action On-Policy Learning with Action-Value Critic
Yuguang Yue, Yunhao Tang, Mingzhang Yin +1
Reinforcement learning (RL) in discrete action space is ubiquitous in real-world applications, but its complexity grows exponentially with the action-space dimension, making it cha…
Meta-Learning without Memorization
Mingzhang Yin, George Tucker, Mingyuan Zhou +2
The ability to learn new concepts with small amounts of data is a critical aspect of intelligence that has proven challenging for deep learning methods. Meta-learning has emerged a…
A Theoretical Case Study of Structured Variational Inference for Community Detection
Mingzhang Yin, Y. X. Rachel Wang, Purnamrita Sarkar
Mean-field variational inference (MFVI) has been widely applied in large scale Bayesian inference. However MFVI, which assumes a product distribution on the latent variables, often…
Semi-Implicit Generative Model
Mingzhang Yin, Mingyuan Zhou
To combine explicit and implicit generative models, we introduce semi-implicit generator (SIG) as a flexible hierarchical model that can be trained in the maximum likelihood framew…
ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical Variables
Mingzhang Yin, Yuguang Yue, Mingyuan Zhou
To address the challenge of backpropagating the gradient through categorical variables, we propose the augment-REINFORCE-swap-merge (ARSM) gradient estimator that is unbiased and h…
Augment-Reinforce-Merge Policy Gradient for Binary Stochastic Policy
Yunhao Tang, Mingzhang Yin, Mingyuan Zhou
Due to the high variance of policy gradients, on-policy optimization algorithms are plagued with low sample efficiency. In this work, we propose Augment-Reinforce-Merge (ARM) polic…
ARM: Augment-REINFORCE-Merge Gradient for Stochastic Binary Networks
Mingzhang Yin, Mingyuan Zhou
To backpropagate the gradients through stochastic binary layers, we propose the augment-REINFORCE-merge (ARM) estimator that is unbiased, exhibits low variance, and has low computa…
Semi-Implicit Variational Inference
Mingzhang Yin, Mingyuan Zhou
Semi-implicit variational inference (SIVI) is introduced to expand the commonly used analytic variational distribution family, by mixing the variational parameter with a flexible d…