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07/18/2023
Nested stochastic block model for simultaneously clustering networks and nodes
We introduce the nested stochastic block model (NSBM) to cluster a colle...
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05/29/2023
A Bayesian sparse factor model with adaptive posterior concentration
In this paper, we propose a new Bayesian inference method for a high-dim...
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04/21/2023
Machine Learning and the Future of Bayesian Computation
Bayesian models are a powerful tool for studying complex data, allowing ...
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03/05/2023
A Semi-Bayesian Nonparametric Hypothesis Test Using Maximum Mean Discrepancy with Applications in Generative Adversarial Networks
A classic inferential problem in statistics is the two-sample hypothesis...
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02/16/2023
Intrinsic and extrinsic deep learning on manifolds
We propose extrinsic and intrinsic deep neural network architectures as ...
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03/04/2022
Bayesian community detection for networks with covariates
The increasing prevalence of network data in a vast variety of fields an...
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12/05/2021
Bayesian Optimal Two-sample Tests in High-dimension
We propose optimal Bayesian two-sample tests for testing equality of hig...
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11/25/2021
Robustness against Adversarial Attacks in Neural Networks using Incremental Dissipativity
Adversarial examples can easily degrade the classification performance i...
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09/24/2021
Scalable Bayesian high-dimensional local dependence learning
In this work, we propose a scalable Bayesian procedure for learning the ...
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09/07/2021
Adaptive variational Bayes: Optimality, computation and applications
In this paper, we explore adaptive inference based on variational Bayes....
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09/04/2021
Training Graph Neural Networks by Graphon Estimation
In this work, we propose to train a graph neural network via resampling ...
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05/09/2021
A likelihood approach to nonparametric estimation of a singular distribution using deep generative models
We investigate statistical properties of a likelihood approach to nonpar...
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11/24/2020
A spectral-based framework for hypothesis testing in populations of networks
In this paper, we propose a new spectral-based approach to hypothesis te...
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10/18/2020
Accelerated Algorithms for Convex and Non-Convex Optimization on Manifolds
We propose a general scheme for solving convex and non-convex optimizati...
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09/29/2020
Community detection, pattern recognition, and hypergraph-based learning: approaches using metric geometry and persistent homology
Hypergraph data appear and are hidden in many places in the modern age. ...
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09/29/2020
Weight Prediction for Variants of Weighted Directed Networks
A weighted directed network (WDN) is a directed graph in which each edge...
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09/08/2020
Neural Time-Dependent Partial Differential Equation
Partial differential equations (PDEs) play a crucial role in studying a ...
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08/30/2020
Bayesian High-dimensional Semi-parametric Inference beyond sub-Gaussian Errors
We consider a sparse linear regression model with unknown symmetric erro...
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08/21/2020
Optimization of Graph Neural Networks with Natural Gradient Descent
In this work, we propose to employ information-geometric tools to optimi...
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07/17/2020
Optimal Bayesian estimation of Gaussian mixtures with growing number of components
We study posterior concentration properties of Bayesian procedures for e...
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06/11/2020
Robust Optimization and Inference on Manifolds
We propose a robust and scalable procedure for general optimization and ...
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04/09/2020
Bayesian classification, anomaly detection, and survival analysis using network inputs with application to the microbiome
While the study of a single network is well-established, technological a...
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11/18/2019
A projection approach for multiple monotone regression
Shape-constrained inference has wide applicability in bioassay, medicine...
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11/09/2019
Hypothesis testing for populations of networks
It has become an increasingly common practice for scientists in modern s...
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08/05/2019
Change-point detection in dynamic networks via graphon estimation
We propose a general approach for change-point detection in dynamic netw...
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03/30/2019
Hierarchical Stochastic Block Model for Community Detection in Multiplex Networks
Multiplex networks have become increasingly more prevalent in many field...
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03/21/2019
Exact slice sampler for Hierarchical Dirichlet Processes
We propose an exact slice sampler for Hierarchical Dirichlet process (HD...
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11/15/2018
Minimax Posterior Convergence Rates and Model Selection Consistency in High-dimensional DAG Models based on Sparse Cholesky Factors
In this paper, we study the high-dimensional sparse directed acyclic gra...
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10/28/2018
Bounded Regression with Gaussian Process Projection
Examples with bound information on the regression function and density a...
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10/26/2018
Communication Efficient Parallel Algorithms for Optimization on Manifolds
The last decade has witnessed an explosion in the development of models,...
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10/18/2018
Hierarchical Network Item Response Modeling for Discovering Differences Between Innovation and Regular School Systems in Korea
The innovation school system has been implemented in Korea to cultivate ...
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10/05/2018
Network Distance Based on Laplacian Flows on Graphs
Distance plays a fundamental role in measuring similarity between object...
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09/10/2018
Maximum Pairwise Bayes Factors for Covariance Structure Testing
Hypothesis testing of structure in covariance matrices is of significant...
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04/23/2018
Bayesian Test and Selection for Bandwidth of High-dimensional Banded Precision Matrices
Assuming a banded structure is one of the common practice in the estimat...
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01/03/2018
Intrinsic Gaussian processes on complex constrained domains
We propose a class of intrinsic Gaussian processes (in-GPs) for interpol...
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01/03/2018
Differential Geometry for Model Independent Analysis of Images and Other Non-Euclidean Data: Recent Developments
This article provides an exposition of recent methodologies for nonparam...
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10/19/2016
Robust and Parallel Bayesian Model Selection
Effective and accurate model selection is an important problem in modern...
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06/08/2016