Search Results for author: Lizhen Lin

Found 26 papers, 9 papers with code

Nested stochastic block model for simultaneously clustering networks and nodes

no code implementations18 Jul 2023 Nathaniel Josephs, Arash A. Amini, Marina Paez, Lizhen Lin

We introduce the nested stochastic block model (NSBM) to cluster a collection of networks while simultaneously detecting communities within each network.

Clustering Stochastic Block Model

A Bayesian sparse factor model with adaptive posterior concentration

no code implementations29 May 2023 Ilsang Ohn, Lizhen Lin, Yongdai Kim

In this paper, we propose a new Bayesian inference method for a high-dimensional sparse factor model that allows both the factor dimensionality and the sparse structure of the loading matrix to be inferred.

Bayesian Inference

Machine Learning and the Future of Bayesian Computation

no code implementations21 Apr 2023 Steven Winter, Trevor Campbell, Lizhen Lin, Sanvesh Srivastava, David B. Dunson

Bayesian models are a powerful tool for studying complex data, allowing the analyst to encode rich hierarchical dependencies and leverage prior information.

Bayesian Inference Variational Inference

Intrinsic and extrinsic deep learning on manifolds

no code implementations16 Feb 2023 Yihao Fang, Ilsang Ohn, Vijay Gupta, Lizhen Lin

We propose extrinsic and intrinsic deep neural network architectures as general frameworks for deep learning on manifolds.

Extrinsic Bayesian Optimizations on Manifolds

no code implementations21 Dec 2022 Yihao Fang, Mu Niu, Pokman Cheung, Lizhen Lin

We propose an extrinsic Bayesian optimization (eBO) framework for general optimization problems on manifolds.

Bayesian Optimization Gaussian Processes +1

Bayesian community detection for networks with covariates

1 code implementation4 Mar 2022 Luyi Shen, Arash Amini, Nathaniel Josephs, Lizhen Lin

The increasing prevalence of network data in a vast variety of fields and the need to extract useful information out of them have spurred fast developments in related models and algorithms.

Community Detection Stochastic Block Model

Training Graph Neural Networks by Graphon Estimation

no code implementations4 Sep 2021 Ziqing Hu, Yihao Fang, Lizhen Lin

In this work, we propose to train a graph neural network via resampling from a graphon estimate obtained from the underlying network data.

Graphon Estimation

A likelihood approach to nonparametric estimation of a singular distribution using deep generative models

no code implementations9 May 2021 Minwoo Chae, Dongha Kim, Yongdai Kim, Lizhen Lin

In the considered model, a usual likelihood approach can fail to estimate the target distribution consistently due to the singularity.

Accelerated Algorithms for Convex and Non-Convex Optimization on Manifolds

no code implementations18 Oct 2020 Lizhen Lin, Bayan Saparbayeva, Michael Minyi Zhang, David B. Dunson

One of the key challenges for optimization on manifolds is the difficulty of verifying the complexity of the objective function, e. g., whether the objective function is convex or non-convex, and the degree of non-convexity.

Weight Prediction for Variants of Weighted Directed Networks

no code implementations29 Sep 2020 Dong Quan Ngoc Nguyen, Lin Xing, Lizhen Lin

We introduce, for the first time, a metric geometry approach to studying edge weight prediction in WDNs.

Graph Embedding

Community detection, pattern recognition, and hypergraph-based learning: approaches using metric geometry and persistent homology

1 code implementation29 Sep 2020 Dong Quan Ngoc Nguyen, Lin Xing, Lizhen Lin

Also based on the topological space structure of hypergraph data introduced in our paper, we introduce a modified nearest neighbors methods which is a generalization of the classical nearest neighbors methods from machine learning.

Community Detection

Neural Time-Dependent Partial Differential Equation

no code implementations28 Sep 2020 Yihao Hu, Tong Zhao, Zhiliang Xu, Lizhen Lin

Inspired by the traditional finite difference and finite elements methods and emerging advancements in machine learning, we propose a sequence-to-sequence learning (Seq2Seq) framework called Neural-PDE, which allows one to automatically learn governing rules of any time-dependent PDE system from existing data by using a bidirectional LSTM encoder, and predict the solutions in next $n$ time steps.

Neural-PDE: A RNN based neural network for solving time dependent PDEs

1 code implementation8 Sep 2020 Yihao Hu, Tong Zhao, Shixin Xu, Zhiliang Xu, Lizhen Lin

Partial differential equations (PDEs) play a crucial role in studying a vast number of problems in science and engineering.

BIG-bench Machine Learning

Optimization of Graph Neural Networks with Natural Gradient Descent

1 code implementation21 Aug 2020 Mohammad Rasool Izadi, Yihao Fang, Robert Stevenson, Lizhen Lin

In this work, we propose to employ information-geometric tools to optimize a graph neural network architecture such as the graph convolutional networks.

Node Classification

Bayesian classification, anomaly detection, and survival analysis using network inputs with application to the microbiome

2 code implementations9 Apr 2020 Nathaniel Josephs, Lizhen Lin, Steven Rosenberg, Eric D. Kolaczyk

While the study of a single network is well-established, technological advances now allow for the collection of multiple networks with relative ease.


Hierarchical Stochastic Block Model for Community Detection in Multiplex Networks

1 code implementation30 Mar 2019 Arash A. Amini, Marina S. Paez, Lizhen Lin

Moreover, our model automatically picks up the necessary number of communities at each layer (as validated by real data examples).

Community Detection Stochastic Block Model

Exact slice sampler for Hierarchical Dirichlet Processes

2 code implementations21 Mar 2019 Arash A. Amini, Marina Paez, Lizhen Lin, Zahra S. Razaee

We propose an exact slice sampler for Hierarchical Dirichlet process (HDP) and its associated mixture models (Teh et al., 2006).

Intrinsic Gaussian processes on complex constrained domains

no code implementations3 Jan 2018 Mu Niu, Pokman Cheung, Lizhen Lin, Zhenwen Dai, Neil Lawrence, David Dunson

in-GPs respect the potentially complex boundary or interior conditions as well as the intrinsic geometry of the spaces.

Gaussian Processes valid

Robust and Parallel Bayesian Model Selection

no code implementations19 Oct 2016 Michael Minyi Zhang, Henry Lam, Lizhen Lin

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

Model Selection Variable Selection

On clustering network-valued data

1 code implementation NeurIPS 2017 Soumendu Sundar Mukherjee, Purnamrita Sarkar, Lizhen Lin

Community detection, which focuses on clustering nodes or detecting communities in (mostly) a single network, is a problem of considerable practical interest and has received a great deal of attention in the research community.

Clustering Community Detection

Robust and Scalable Bayes via a Median of Subset Posterior Measures

no code implementations11 Mar 2014 Stanislav Minsker, Sanvesh Srivastava, Lizhen Lin, David B. Dunson

We propose a novel approach to Bayesian analysis that is provably robust to outliers in the data and often has computational advantages over standard methods.

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