Mingyuan Zhou

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

Assistant Professor of Statistics at The University of Texas at Austin

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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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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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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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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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. ∙ 4 ∙ 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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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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Infinite Edge Partition Models for Overlapping Community Detection and Link Prediction

A hierarchical gamma process infinite edge partition model is proposed t...
01/25/2015 ∙ by Mingyuan Zhou, et al. ∙ 0 ∙ share

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Beta-Negative Binomial Process and Exchangeable Random Partitions for Mixed-Membership Modeling

The beta-negative binomial process (BNBP), an integer-valued stochastic ...
10/28/2014 ∙ by Mingyuan Zhou, et al. ∙ 0 ∙ share

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Priors for Random Count Matrices Derived from a Family of Negative Binomial Processes

We define a family of probability distributions for random count matrice...
04/12/2014 ∙ by Mingyuan Zhou, et al. ∙ 0 ∙ share

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Generalized Negative Binomial Processes and the Representation of Cluster Structures

The paper introduces the concept of a cluster structure to define a join...
10/07/2013 ∙ by Mingyuan Zhou, et al. ∙ 0 ∙ share

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A Bayesian Nonparametric Approach to Image Super-resolution

Super-resolution methods form high-resolution images from low-resolution...
09/22/2012 ∙ by Gungor Polatkan, et al. ∙ 0 ∙ share

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Negative Binomial Process Count and Mixture Modeling

The seemingly disjoint problems of count and mixture modeling are united...
09/15/2012 ∙ by Mingyuan Zhou, et al. ∙ 0 ∙ share

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Augment-and-Conquer Negative Binomial Processes

By developing data augmentation methods unique to the negative binomial ...
09/05/2012 ∙ by Mingyuan Zhou, et al. ∙ 0 ∙ share

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Beta-Negative Binomial Process and Poisson Factor Analysis

A beta-negative binomial (BNB) process is proposed, leading to a beta-ga...
12/15/2011 ∙ by Mingyuan Zhou, et al. ∙ 0 ∙ share

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Nested Dictionary Learning for Hierarchical Organization of Imagery and Text

A tree-based dictionary learning model is developed for joint analysis o...
10/16/2012 ∙ by Lingbo Li, 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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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 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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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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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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Mingyuan Zhou

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