b'Lizhen Lin'

research

∙ 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...

0 Ilsang Ohn, et al. ∙

research

∙ 04/21/2023

Machine Learning and the Future of Bayesian Computation

Bayesian models are a powerful tool for studying complex data, allowing ...

0 Steven Winter, et al. ∙

research

∙ 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...

0 Forough Fazeli Asl, et al. ∙

research

∙ 02/16/2023

Intrinsic and extrinsic deep learning on manifolds

We propose extrinsic and intrinsic deep neural network architectures as ...

0 Yihao Fang, et al. ∙

research

∙ 03/04/2022

Bayesian community detection for networks with covariates

The increasing prevalence of network data in a vast variety of fields an...

0 Luyi Shen, et al. ∙

research

∙ 12/05/2021

Bayesian Optimal Two-sample Tests in High-dimension

We propose optimal Bayesian two-sample tests for testing equality of hig...

0 Kyoungjae Lee, et al. ∙

research

∙ 11/25/2021

Robustness against Adversarial Attacks in Neural Networks using Incremental Dissipativity

Adversarial examples can easily degrade the classification performance i...

6 Bernardo Aquino, et al. ∙

research

∙ 09/24/2021

Scalable Bayesian high-dimensional local dependence learning

In this work, we propose a scalable Bayesian procedure for learning the ...

0 Kyoungjae Lee, et al. ∙

research

∙ 09/07/2021

Adaptive variational Bayes: Optimality, computation and applications

In this paper, we explore adaptive inference based on variational Bayes....

15 Ilsang Ohn, et al. ∙

research

∙ 09/04/2021

Training Graph Neural Networks by Graphon Estimation

In this work, we propose to train a graph neural network via resampling ...

0 Ziqing Hu, et al. ∙

research

∙ 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...

8 Minwoo Chae, et al. ∙

research

∙ 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...

0 Li Chen, et al. ∙

research

∙ 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...

22 Lizhen Lin, et al. ∙

research

∙ 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. ...

0 Dong Quan Ngoc Nguyen, et al. ∙

research

∙ 09/29/2020

Weight Prediction for Variants of Weighted Directed Networks

A weighted directed network (WDN) is a directed graph in which each edge...

0 Dong Quan Ngoc Nguyen, et al. ∙

research

∙ 09/08/2020

Neural Time-Dependent Partial Differential Equation

Partial differential equations (PDEs) play a crucial role in studying a ...

16 Yihao Hu, et al. ∙

research

∙ 08/30/2020

Bayesian High-dimensional Semi-parametric Inference beyond sub-Gaussian Errors

We consider a sparse linear regression model with unknown symmetric erro...

0 Kyoungjae Lee, et al. ∙

research

∙ 08/21/2020

Optimization of Graph Neural Networks with Natural Gradient Descent

In this work, we propose to employ information-geometric tools to optimi...

0 Mohammad Rasool Izadi, et al. ∙

research

∙ 07/17/2020

Optimal Bayesian estimation of Gaussian mixtures with growing number of components

We study posterior concentration properties of Bayesian procedures for e...

0 Ilsang Ohn, et al. ∙

research

∙ 06/11/2020

Robust Optimization and Inference on Manifolds

We propose a robust and scalable procedure for general optimization and ...

0 Lizhen Lin, et al. ∙

research

∙ 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...

0 Nathaniel Josephs, et al. ∙

research

∙ 11/18/2019

A projection approach for multiple monotone regression

Shape-constrained inference has wide applicability in bioassay, medicine...

0 Lizhen Lin, et al. ∙

research

∙ 11/09/2019

Hypothesis testing for populations of networks

It has become an increasingly common practice for scientists in modern s...

0 Li Chen, et al. ∙

research

∙ 08/05/2019

Change-point detection in dynamic networks via graphon estimation

We propose a general approach for change-point detection in dynamic netw...

0 Zifeng Zhao, et al. ∙

research

∙ 03/30/2019

Hierarchical Stochastic Block Model for Community Detection in Multiplex Networks

Multiplex networks have become increasingly more prevalent in many field...

0 Marina S. Paez, et al. ∙

research

∙ 03/21/2019

Exact slice sampler for Hierarchical Dirichlet Processes

We propose an exact slice sampler for Hierarchical Dirichlet process (HD...

0 Arash A. Amini, et al. ∙

research

∙ 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...

0 Kyoungjae Lee, et al. ∙

research

∙ 10/28/2018

Bounded Regression with Gaussian Process Projection

Examples with bound information on the regression function and density a...

0 Jize Zhang, et al. ∙

research

∙ 10/26/2018

Communication Efficient Parallel Algorithms for Optimization on Manifolds

The last decade has witnessed an explosion in the development of models,...

0 Bayan Saparbayeva, et al. ∙

research

∙ 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 ...

0 Ick Hoon Jin, et al. ∙

research

∙ 10/05/2018

Network Distance Based on Laplacian Flows on Graphs

Distance plays a fundamental role in measuring similarity between object...

0 Dianbin Bao, et al. ∙

research

∙ 09/10/2018

Maximum Pairwise Bayes Factors for Covariance Structure Testing

Hypothesis testing of structure in covariance matrices is of significant...

0 Kyoungjae Lee, et al. ∙

research

∙ 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...

0 Kyoungjae Lee, et al. ∙

research

∙ 01/03/2018

Intrinsic Gaussian processes on complex constrained domains

We propose a class of intrinsic Gaussian processes (in-GPs) for interpol...

0 Mu Niu, et al. ∙

research

∙ 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...

0 Rabi Bhattacharya, et al. ∙

research

∙ 10/19/2016

Robust and Parallel Bayesian Model Selection

Effective and accurate model selection is an important problem in modern...

0 Michael Minyi Zhang, et al. ∙

research

∙ 06/08/2016

On clustering network-valued data

Community detection, which focuses on clustering nodes or detecting comm...

0 Soumendu Sundar Mukherjee, et al. ∙

Lizhen Lin

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