Search Results for author: Mingzhang Yin

Found 13 papers, 7 papers with code

Partial Identification with Noisy Covariates: A Robust Optimization Approach

no code implementations22 Feb 2022 Wenshuo Guo, Mingzhang Yin, Yixin Wang, Michael I. Jordan

Directly adjusting for these imperfect measurements of the covariates can lead to biased causal estimates.

Causal Inference

Probabilistic Best Subset Selection via Gradient-Based Optimization

1 code implementation11 Jun 2020 Mingzhang Yin, Nhat Ho, Bowei Yan, Xiaoning Qian, Mingyuan Zhou

In high-dimensional statistics, variable selection is an optimization problem aiming to recover the latent sparse pattern from all possible covariate combinations.

Methodology

Pairwise Supervised Hashing with Bernoulli Variational Auto-Encoder and Self-Control Gradient Estimator

no code implementations21 May 2020 Siamak Zamani Dadaneh, Shahin Boluki, Mingzhang Yin, Mingyuan Zhou, Xiaoning Qian

Semantic hashing has become a crucial component of fast similarity search in many large-scale information retrieval systems, in particular, for text data.

Information Retrieval

Discrete Action On-Policy Learning with Action-Value Critic

1 code implementation10 Feb 2020 Yuguang Yue, Yunhao Tang, Mingzhang Yin, Mingyuan Zhou

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 challenging to apply existing on-policy gradient based deep RL algorithms efficiently.

OpenAI Gym

Meta-Learning without Memorization

1 code implementation ICLR 2020 Mingzhang Yin, George Tucker, Mingyuan Zhou, Sergey Levine, Chelsea Finn

If this is not done, the meta-learner can ignore the task training data and learn a single model that performs all of the meta-training tasks zero-shot, but does not adapt effectively to new image classes.

Few-Shot Image Classification Meta-Learning

Semi-Implicit Generative Model

no code implementations29 May 2019 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 framework.

ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical Variables

1 code implementation4 May 2019 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 has low variance.

Augment-Reinforce-Merge Policy Gradient for Binary Stochastic Policy

no code implementations13 Mar 2019 Yunhao Tang, Mingzhang Yin, Mingyuan Zhou

Due to the high variance of policy gradients, on-policy optimization algorithms are plagued with low sample efficiency.

ARM: Augment-REINFORCE-Merge Gradient for Stochastic Binary Networks

1 code implementation ICLR 2019 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 computational complexity.

Data Augmentation Variational Inference

Semi-Implicit Variational Inference

1 code implementation ICML 2018 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 distribution.

Bayesian Inference Variational Inference

Convergence of Gradient EM on Multi-component Mixture of Gaussians

no code implementations NeurIPS 2017 Bowei Yan, Mingzhang Yin, Purnamrita Sarkar

In this paper, we study convergence properties of the gradient variant of Expectation-Maximization algorithm~\cite{lange1995gradient} for Gaussian Mixture Models for arbitrary number of clusters and mixing coefficients.

Learning Theory

Convergence Analysis of Gradient EM for Multi-component Gaussian Mixture

no code implementations23 May 2017 Bowei Yan, Mingzhang Yin, Purnamrita Sarkar

In this paper, we study convergence properties of the gradient Expectation-Maximization algorithm \cite{lange1995gradient} for Gaussian Mixture Models for general number of clusters and mixing coefficients.

Learning Theory

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