
Graph Gamma Process Generalized Linear Dynamical Systems
We introduce graph gamma process (GGP) linear dynamical systems to model...
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Implicit Distributional Reinforcement Learning
To improve the sample efficiency of policygradient based reinforcement ...
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Probabilistic Best Subset Selection via GradientBased Optimization
In highdimensional statistics, variable selection is an optimization pr...
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Probabilistic Best Subset Selection by GradientBased Optimization
In highdimensional statistics, variable selection is an optimization pr...
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Bayesian Graph Neural Networks with Adaptive Connection Sampling
We propose a unified framework for adaptive connection sampling in graph...
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Pairwise Supervised Hashing with Bernoulli Variational AutoEncoder and SelfControl Gradient Estimator
Semantic hashing has become a crucial component of fast similarity searc...
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Mutual Information Gradient Estimation for Representation Learning
Mutual Information (MI) plays an important role in representation learni...
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Learnable Bernoulli Dropout for Bayesian Deep Learning
In this work, we propose learnable Bernoulli dropout (LBD), a new model...
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Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation
Sequence generation models are commonly refined with reinforcement learn...
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Recurrent Hierarchical TopicGuided Neural Language Models
To simultaneously capture syntax and global semantics from a text corpus...
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MetaLearning without Memorization
The ability to learn new concepts with small amounts of data is a critic...
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Weibull Racing Timetoevent Modeling and Analysis of Online Borrowers' Loan Payoff and Default
We propose Weibull delegate racing (WDR) to explicitly model surviving u...
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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...
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Thompson Sampling via Local Uncertainty
Thompson sampling is an efficient algorithm for sequential decision maki...
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PoissonRandomized Gamma Dynamical Systems
This paper presents the Poissonrandomized gamma dynamical system (PRGDS...
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SemiImplicit Stochastic Recurrent Neural Networks
Stochastic recurrent neural networks with latent random variables of com...
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Semisupervised Learning using Adversarial Training with Good and Bad Samples
In this work, we investigate semisupervised learning (SSL) for image cl...
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Variational Graph Recurrent Neural Networks
Representation learning over graph structured data has been mostly studi...
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SemiImplicit Graph Variational AutoEncoders
Semiimplicit graph variational autoencoder (SIGVAE) is proposed to ex...
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Bayesian GammaNegative Binomial Modeling of SingleCell RNA Sequencing Data
Background: Singlecell RNA sequencing (scRNAseq) is a powerful profili...
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SemiImplicit Generative Model
To combine explicit and implicit generative models, we introduce semiim...
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Variational HeteroEncoder Randomized Generative Adversarial Networks for Joint ImageText Modeling
For bidirectional joint imagetext modeling, we develop variational hete...
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Convolutional Poisson Gamma Belief Network
For text analysis, one often resorts to a lossy representation that eith...
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ARSM: AugmentREINFORCESwapMerge Estimator for Gradient Backpropagation Through Categorical Variables
To address the challenge of backpropagating the gradient through categor...
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Deep Generative Models for Sparse, Highdimensional, and Overdispersed Discrete Data
Many applications, such as text modelling, highthroughput sequencing, a...
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NonLambertian Surface Shape and Reflectance Reconstruction Using Concentric MultiSpectral Light Field
Recovering the shape and reflectance of nonLambertian surfaces remains ...
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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 ...
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AugmentReinforceMerge Policy Gradient for Binary Stochastic Policy
Due to the high variance of policy gradients, onpolicy optimization alg...
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Dirichlet belief networks for topic structure learning
Recently, considerable research effort has been devoted to developing de...
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Deep Poisson gamma dynamical systems
We develop deep Poissongamma dynamical systems (DPGDS) to model sequent...
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Bayesian multidomain learning for cancer subtype discovery from nextgeneration sequencing count data
Precision medicine aims for personalized prognosis and therapeutics by u...
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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...
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ARM: AugmentREINFORCEMerge Gradient for Discrete Latent Variable Models
To backpropagate the gradients through discrete stochastic layers, we en...
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SemiImplicit Variational Inference
Semiimplicit variational inference (SIVI) is introduced to expand the c...
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Parsimonious Bayesian deep networks
Combining Bayesian nonparametrics and a forward model selection strategy...
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Masking: A New Perspective of Noisy Supervision
It is important to learn classifiers under noisy labels due to their ubi...
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Locally Private Bayesian Inference for Count Models
As more aspects of social interaction are digitally recorded, there is a...
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Differential Expression Analysis of Dynamical Sequencing Count Data with a Gamma Markov Chain
Nextgeneration sequencing (NGS) to profile temporal changes in living s...
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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...
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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...
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Nonparametric Bayesian Sparse Graph Linear Dynamical Systems
A nonparametric Bayesian sparse graph linear dynamical system (SGLDS) is...
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Deep Latent Dirichlet Allocation with TopicLayerAdaptive Stochastic Gradient Riemannian MCMC
It is challenging to develop stochastic gradient based scalable inferenc...
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PoissonGamma Dynamical Systems
We introduce a new dynamical system for sequentially observed multivaria...
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Permuted and Augmented StickBreaking Bayesian Multinomial Regression
To model categorical response variables given their covariates, we propo...
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Softplus Regressions and Convex Polytopes
To construct flexible nonlinear predictive distributions, the paper intr...
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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...
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Nonparametric Bayesian Negative Binomial Factor Analysis
A common approach to analyze a covariatesample count matrix, an element...
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Nonparametric Bayesian Factor Analysis for Dynamic Count Matrices
A gamma process dynamic Poisson factor analysis model is proposed to fac...
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Gamma Belief Networks
To infer multilayer deep representations of highdimensional discrete an...
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The Poisson Gamma Belief Network
To infer a multilayer representation of highdimensional count vectors, ...
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Mingyuan Zhou
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Assistant Professor of Statistics at The University of Texas at Austin