Mingyuan Zhou

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Duke University ∙ The University of Texas at Austin ∙ Microsoft ∙ University of Delaware

Assistant Professor of Statistics at The University of Texas at Austin

Graph Gamma Process Generalized Linear Dynamical Systems

We introduce graph gamma process (GGP) linear dynamical systems to model...
07/25/2020 ∙ by Rahi Kalantari, et al. ∙ 0 ∙ share

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Implicit Distributional Reinforcement Learning

To improve the sample efficiency of policy-gradient based reinforcement ...
07/13/2020 ∙ by Yuguang Yue, et al. ∙ 23 ∙ share

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Probabilistic Best Subset Selection via Gradient-Based Optimization

In high-dimensional statistics, variable selection is an optimization pr...
06/11/2020 ∙ by Mingzhang Yin, et al. ∙ 0 ∙ share

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Probabilistic Best Subset Selection by Gradient-Based Optimization

In high-dimensional statistics, variable selection is an optimization pr...
06/11/2020 ∙ by Mingzhang Yin, et al. ∙ 0 ∙ share

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Bayesian Graph Neural Networks with Adaptive Connection Sampling

We propose a unified framework for adaptive connection sampling in graph...
06/07/2020 ∙ by Arman Hasanzadeh, et al. ∙ 19 ∙ share

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

Semantic hashing has become a crucial component of fast similarity searc...
05/21/2020 ∙ by Siamak Zamani Dadaneh, et al. ∙ 11 ∙ share

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Mutual Information Gradient Estimation for Representation Learning

Mutual Information (MI) plays an important role in representation learni...
05/03/2020 ∙ by Liangjian Wen, et al. ∙ 6 ∙ share

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Learnable Bernoulli Dropout for Bayesian Deep Learning

In this work, we propose learnable Bernoulli dropout (LBD), a new model-...
02/12/2020 ∙ by Shahin Boluki, et al. ∙ 0 ∙ share

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Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation

Sequence generation models are commonly refined with reinforcement learn...
12/31/2019 ∙ by Xinjie Fan, et al. ∙ 21 ∙ share

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Recurrent Hierarchical Topic-Guided Neural Language Models

To simultaneously capture syntax and global semantics from a text corpus...
12/21/2019 ∙ by Dandan Guo, et al. ∙ 0 ∙ share

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Meta-Learning without Memorization

The ability to learn new concepts with small amounts of data is a critic...
12/09/2019 ∙ by Mingzhang Yin, et al. ∙ 19 ∙ share

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Weibull Racing Time-to-event Modeling and Analysis of Online Borrowers' Loan Payoff and Default

We propose Weibull delegate racing (WDR) to explicitly model surviving u...
11/02/2019 ∙ by Quan Zhang, et al. ∙ 0 ∙ share

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ARSM Gradient Estimator for Supervised Learning to Rank

We propose a new model for supervised learning to rank. In our model, th...
11/01/2019 ∙ by Siamak Zamani Dadaneh, et al. ∙ 0 ∙ share

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Thompson Sampling via Local Uncertainty

Thompson sampling is an efficient algorithm for sequential decision maki...
10/30/2019 ∙ by Zhendong Wang, et al. ∙ 0 ∙ share

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Poisson-Randomized Gamma Dynamical Systems

This paper presents the Poisson-randomized gamma dynamical system (PRGDS...
10/28/2019 ∙ by Aaron Schein, et al. ∙ 34 ∙ share

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Semi-Implicit Stochastic Recurrent Neural Networks

Stochastic recurrent neural networks with latent random variables of com...
10/28/2019 ∙ by Ehsan Hajiramezanali, et al. ∙ 0 ∙ share

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Semi-supervised Learning using Adversarial Training with Good and Bad Samples

In this work, we investigate semi-supervised learning (SSL) for image cl...
10/18/2019 ∙ by Wenyuan Li, et al. ∙ 20 ∙ share

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Variational Graph Recurrent Neural Networks

Representation learning over graph structured data has been mostly studi...
08/26/2019 ∙ by Ehsan Hajiramezanali, et al. ∙ 0 ∙ share

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Semi-Implicit Graph Variational Auto-Encoders

Semi-implicit graph variational auto-encoder (SIG-VAE) is proposed to ex...
08/19/2019 ∙ by Arman Hasanzadeh, et al. ∙ 0 ∙ share

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Bayesian Gamma-Negative Binomial Modeling of Single-Cell RNA Sequencing Data

Background: Single-cell RNA sequencing (scRNA-seq) is a powerful profili...
08/01/2019 ∙ by Siamak Zamani Dadaneh, et al. ∙ 0 ∙ share

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Semi-Implicit Generative Model

To combine explicit and implicit generative models, we introduce semi-im...
05/29/2019 ∙ by Mingzhang Yin, et al. ∙ 0 ∙ share

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Variational Hetero-Encoder Randomized Generative Adversarial Networks for Joint Image-Text Modeling

For bidirectional joint image-text modeling, we develop variational hete...
05/18/2019 ∙ by Hao Zhang, et al. ∙ 4 ∙ share

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Convolutional Poisson Gamma Belief Network

For text analysis, one often resorts to a lossy representation that eith...
05/14/2019 ∙ by Chaojie Wang, et al. ∙ 0 ∙ share

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

To address the challenge of backpropagating the gradient through categor...
05/04/2019 ∙ by Mingzhang Yin, et al. ∙ 0 ∙ share

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Deep Generative Models for Sparse, High-dimensional, and Overdispersed Discrete Data

Many applications, such as text modelling, high-throughput sequencing, a...
05/02/2019 ∙ by He Zhao, et al. ∙ 0 ∙ share

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Non-Lambertian Surface Shape and Reflectance Reconstruction Using Concentric Multi-Spectral Light Field

Recovering the shape and reflectance of non-Lambertian surfaces remains ...
04/09/2019 ∙ by Mingyuan Zhou, et al. ∙ 0 ∙ share

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3D Face Reconstruction Using Color Photometric Stereo with Uncalibrated Near Point Lights

We present a new color photometric stereo (CPS) method that can recover ...
04/04/2019 ∙ by Zhang Chen, et al. ∙ 0 ∙ share

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Augment-Reinforce-Merge Policy Gradient for Binary Stochastic Policy

Due to the high variance of policy gradients, on-policy optimization alg...
03/13/2019 ∙ by Yunhao Tang, et al. ∙ 5 ∙ share

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Dirichlet belief networks for topic structure learning

Recently, considerable research effort has been devoted to developing de...
11/02/2018 ∙ by He Zhao, et al. ∙ 0 ∙ share

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Deep Poisson gamma dynamical systems

We develop deep Poisson-gamma dynamical systems (DPGDS) to model sequent...
10/26/2018 ∙ by Dandan Guo, et al. ∙ 0 ∙ share

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Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data

Precision medicine aims for personalized prognosis and therapeutics by u...
10/22/2018 ∙ by Ehsan Hajiramezanali, et al. ∙ 0 ∙ share

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Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks

We propose Lomax delegate racing (LDR) to explicitly model the mechanism...
10/19/2018 ∙ by Quan Zhang, et al. ∙ 0 ∙ share

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ARM: Augment-REINFORCE-Merge Gradient for Discrete Latent Variable Models

To backpropagate the gradients through discrete stochastic layers, we en...
07/30/2018 ∙ by Mingzhang Yin, et al. ∙ 0 ∙ share

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Semi-Implicit Variational Inference

Semi-implicit variational inference (SIVI) is introduced to expand the c...
05/28/2018 ∙ by Mingzhang Yin, et al. ∙ 0 ∙ share

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Parsimonious Bayesian deep networks

Combining Bayesian nonparametrics and a forward model selection strategy...
05/22/2018 ∙ by Mingyuan Zhou, et al. ∙ 0 ∙ share

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Masking: A New Perspective of Noisy Supervision

It is important to learn classifiers under noisy labels due to their ubi...
05/21/2018 ∙ by Bo Han, et al. ∙ 0 ∙ share

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Locally Private Bayesian Inference for Count Models

As more aspects of social interaction are digitally recorded, there is a...
03/22/2018 ∙ by Aaron Schein, et al. ∙ 0 ∙ share

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Differential Expression Analysis of Dynamical Sequencing Count Data with a Gamma Markov Chain

Next-generation sequencing (NGS) to profile temporal changes in living s...
03/07/2018 ∙ by Ehsan Hajiramezanali, et al. ∙ 0 ∙ share

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WHAI: Weibull Hybrid Autoencoding Inference for Deep Topic Modeling

To train an inference network jointly with a deep generative topic model...
03/04/2018 ∙ by Hao Zhang, et al. ∙ 0 ∙ share

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Discussion on "Sparse graphs using exchangeable random measures" by Francois Caron and Emily B. Fox

This is a discussion on "Sparse graphs using exchangeable random measure...
02/21/2018 ∙ by Mingyuan Zhou, et al. ∙ 0 ∙ share

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Nonparametric Bayesian Sparse Graph Linear Dynamical Systems

A nonparametric Bayesian sparse graph linear dynamical system (SGLDS) is...
02/21/2018 ∙ by Rahi Kalantari, et al. ∙ 0 ∙ share

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Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian MCMC

It is challenging to develop stochastic gradient based scalable inferenc...
06/06/2017 ∙ by Yulai Cong, et al. ∙ 0 ∙ share

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Poisson--Gamma Dynamical Systems

We introduce a new dynamical system for sequentially observed multivaria...
01/19/2017 ∙ by Aaron Schein, et al. ∙ 0 ∙ share

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Permuted and Augmented Stick-Breaking Bayesian Multinomial Regression

To model categorical response variables given their covariates, we propo...
12/30/2016 ∙ by Quan Zhang, et al. ∙ 0 ∙ share

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Softplus Regressions and Convex Polytopes

To construct flexible nonlinear predictive distributions, the paper intr...
08/23/2016 ∙ by Mingyuan Zhou, et al. ∙ 0 ∙ share

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Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations

We introduce Bayesian Poisson Tucker decomposition (BPTD) for modeling c...
06/06/2016 ∙ by Aaron Schein, et al. ∙ 0 ∙ share

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Nonparametric Bayesian Negative Binomial Factor Analysis

A common approach to analyze a covariate-sample count matrix, an element...
04/25/2016 ∙ by Mingyuan Zhou, et al. ∙ 0 ∙ share

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Nonparametric Bayesian Factor Analysis for Dynamic Count Matrices

A gamma process dynamic Poisson factor analysis model is proposed to fac...
12/30/2015 ∙ by Ayan Acharya, et al. ∙ 0 ∙ share

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Gamma Belief Networks

To infer multilayer deep representations of high-dimensional discrete an...
12/09/2015 ∙ by Mingyuan Zhou, et al. ∙ 0 ∙ share

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The Poisson Gamma Belief Network

To infer a multilayer representation of high-dimensional count vectors, ...
11/06/2015 ∙ by Mingyuan Zhou, et al. ∙ 0 ∙ share

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Mingyuan Zhou

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