no code implementations • 22 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.
1 code implementation • 24 Sep 2021 • Mingzhang Yin, Yixin Wang, David M. Blei
This paper presents a new optimization approach to causal estimation.
1 code implementation • 11 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
no code implementations • 21 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.
1 code implementation • 10 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.
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.
no code implementations • 29 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.
1 code implementation • 4 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.
no code implementations • 13 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.
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.
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.
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.
no code implementations • 23 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.