Carey E. Priebe

research

∙ 09/16/2023

A Statistical Turing Test for Generative Models

The emergence of human-like abilities of AI systems for content generati...

0 Hayden Helm, et al. ∙

research

∙ 08/25/2023

Gotta match 'em all: Solution diversification in graph matching matched filters

We present a novel approach for finding multiple noisily embedded templa...

0 Zhirui Li, et al. ∙

research

∙ 05/10/2023

Occam Factor for Random Graphs: Erdös-Rényi, Independent Edge, and a Uniparametric Stochastic Blockmodel

We investigate the evidence/flexibility (i.e., "Occam") paradigm and dem...

0 Tianyu Wang, et al. ∙

research

∙ 05/09/2023

Comparing Foundation Models using Data Kernels

Recent advances in self-supervised learning and neural network scaling h...

0 Brandon Duderstadt, et al. ∙

research

∙ 05/04/2023

Semisupervised regression in latent structure networks on unknown manifolds

Random graphs are increasingly becoming objects of interest for modeling...

0 Aranyak Acharyya, et al. ∙

research

∙ 05/03/2023

Discovering Communication Pattern Shifts in Large-Scale Networks using Encoder Embedding and Vertex Dynamics

The analysis of large-scale time-series network data, such as social med...

0 Cencheng Shen, et al. ∙

research

∙ 04/18/2023

Independence testing for inhomogeneous random graphs

Testing for independence between graphs is a problem that arises natural...

0 Yukun Song, et al. ∙

research

∙ 03/31/2023

Synergistic Graph Fusion via Encoder Embedding

In this paper, we introduce a novel approach to multi-graph embedding ca...

0 Cencheng Shen, et al. ∙

research

∙ 02/27/2023

Approximately optimal domain adaptation with Fisher's Linear Discriminant Analysis

We propose a class of models based on Fisher's Linear Discriminant (FLD)...

0 Hayden S. Helm, et al. ∙

research

∙ 01/18/2023

Graph Encoder Ensemble for Simultaneous Vertex Embedding and Community Detection

In this paper we propose a novel and computationally efficient method to...

0 Cencheng Shen, et al. ∙

research

∙ 10/26/2022

Deep Learning is Provably Robust to Symmetric Label Noise

Deep neural networks (DNNs) are capable of perfectly fitting the trainin...

0 Carey E. Priebe, et al. ∙

research

∙ 09/24/2022

From Local to Global: Spectral-Inspired Graph Neural Networks

Graph Neural Networks (GNNs) are powerful deep learning methods for Non-...

16 Ningyuan Huang, et al. ∙

research

∙ 08/29/2022

Dynamic Network Sampling for Community Detection

We propose a dynamic network sampling scheme to optimize block recovery ...

0 Cong Mu, et al. ∙

research

∙ 08/23/2022

The Value of Out-of-Distribution Data

More data helps us generalize to a task. But real datasets can contain o...

9 Ashwin De Silva, et al. ∙

research

∙ 05/28/2022

Deep Learning with Label Noise: A Hierarchical Approach

Deep neural networks are susceptible to label noise. Existing methods to...

0 Li Chen, et al. ∙

research

∙ 05/13/2022

Discovering underlying dynamics in time series of networks

Understanding dramatic changes in the evolution of networks is central t...

0 Avanti Athreya, et al. ∙

research

∙ 03/17/2022

ART-SS: An Adaptive Rejection Technique for Semi-Supervised restoration for adverse weather-affected images

In recent years, convolutional neural network-based single image adverse...

0 Rajeev Yasarla, et al. ∙

research

∙ 02/25/2022

Mental State Classification Using Multi-graph Features

We consider the problem of extracting features from passive, multi-chann...

0 Guodong Chen, et al. ∙

research

∙ 01/19/2022

Prospective Learning: Back to the Future

Research on both natural intelligence (NI) and artificial intelligence (...

8 Joshua T. Vogelstein, et al. ∙

research

∙ 11/09/2021

Graph Matching via Optimal Transport

The graph matching problem seeks to find an alignment between the nodes ...

20 Ali Saad-Eldin, et al. ∙

research

∙ 09/29/2021

Towards a theory of out-of-distribution learning

What is learning? 20^st century formalizations of learning theory – whic...

55 Ali Geisa, et al. ∙

research

∙ 09/27/2021

Graph Encoder Embedding

In this paper we propose a lightning fast graph embedding method called ...

57 Cencheng Shen, et al. ∙

research

∙ 07/23/2021

A principled (and practical) test for network comparison

How might one test the hypothesis that graphs were sampled from the same...

0 Gecia Bravo Hermsdorff, et al. ∙

research

∙ 06/23/2021

Leveraging semantically similar queries for ranking via combining representations

In modern ranking problems, different and disparate representations of t...

0 Hayden S. Helm, et al. ∙

research

∙ 05/04/2021

Occam Factor for Gaussian Models With Unknown Variance Structure

We discuss model selection to determine whether the variance-covariance ...

0 Zachary M. Pisano, et al. ∙

research

∙ 04/01/2021

Dynamic Silos: Modularity in intra-organizational communication networks before and during the Covid-19 pandemic

Workplace communications around the world were drastically altered by Co...

7 Jonathan Larson, et al. ∙

research

∙ 03/26/2021

Beyond the adjacency matrix: random line graphs and inference for networks with edge attributes

Any modern network inference paradigm must incorporate multiple aspects ...

0 Zachary Lubberts, et al. ∙

research

∙ 02/20/2021

Inducing a hierarchy for multi-class classification problems

In applications where categorical labels follow a natural hierarchy, cla...

4 Hayden S. Helm, et al. ∙

research

∙ 01/29/2021

Subgraph nomination: Query by Example Subgraph Retrieval in Networks

This paper introduces the subgraph nomination inference task, in which e...

5 Al-Fahad M. Al-Qadhi, et al. ∙

research

∙ 11/12/2020

A partition-based similarity for classification distributions

Herein we define a measure of similarity between classification distribu...

6 Hayden S. Helm, et al. ∙

research

∙ 10/25/2020

A Simple Spectral Failure Mode for Graph Convolutional Networks

We present a simple generative model in which spectral graph embedding f...

2 Carey E. Priebe, et al. ∙

research

∙ 10/24/2020

Vertex nomination between graphs via spectral embedding and quadratic programming

Given a network and a subset of interesting vertices whose identities ar...

0 Runbing Zheng, et al. ∙

research

∙ 08/23/2020

Multiple Network Embedding for Anomaly Detection in Time Series of Graphs

This paper considers the graph signal processing problem of anomaly dete...

0 Guodong Chen, et al. ∙

research

∙ 08/21/2020

Correcting a Nonparametric Two-sample Graph Hypothesis Test for Graphs with Different Numbers of Vertices

Random graphs are statistical models that have many applications, rangin...

0 Anton A. Alyakin, et al. ∙

research

∙ 07/04/2020

On identifying unobserved heterogeneity in stochastic blockmodel graphs with vertex covariates

Both observed and unobserved vertex heterogeneity can influence block st...

0 Cong Mu, et al. ∙

research

∙ 05/20/2020

Learning to rank via combining representations

Learning to rank – producing a ranked list of items specific to a query ...

13 Hayden S. Helm, et al. ∙

research

∙ 04/29/2020

On the role of features in vertex nomination: Content and context together are better (sometimes)

Vertex nomination is a lightly-supervised network information retrieval ...

0 Keith Levin, et al. ∙

research

∙ 04/27/2020

A general approach to progressive intelligence

In biological learning, data is used to improve performance on the task ...

42 Joshua T. Vogelstein, et al. ∙

research

∙ 04/27/2020

A general approach to progressive learning

In biological learning, data is used to improve performance on the task ...

6 Joshua T. Vogelstein, et al. ∙

research

∙ 04/15/2020

Learning 1-Dimensional Submanifolds for Subsequent Inference on Random Dot Product Graphs

A random dot product graph (RDPG) is a generative model for networks in ...

0 Michael W. Trosset, et al. ∙

research

∙ 03/31/2020

On Two Distinct Sources of Nonidentifiability in Latent Position Random Graph Models

Two separate and distinct sources of nonidentifiability arise naturally ...

0 Joshua Agterberg, et al. ∙

research

∙ 03/30/2020

Spectral graph clustering via the Expectation-Solution algorithm

The stochastic blockmodel (SBM) models the connectivity within and betwe...

0 Zachary M. Pisano, et al. ∙

research

∙ 02/23/2020

On a complete and sufficient statistic for the correlated Bernoulli random graph model

Inference on vertex-aligned graphs is of wide theoretical and practical ...

0 Donniell E. Fishkind, et al. ∙

research

∙ 02/05/2020

Graph matching between bipartite and unipartite networks: to collapse, or not to collapse, that is the question

Graph matching consists of aligning the vertices of two unlabeled graphs...

0 Jesús Arroyo, et al. ∙

research

∙ 11/27/2019

LqRT: Robust Hypothesis Testing of Location Parameters using Lq-Likelihood-Ratio-Type Test in Python

A t-test is considered a standard procedure for inference on population ...

0 Anton Alyakin, et al. ∙

research

∙ 11/18/2019

Change point localization in dependent dynamic nonparametric random dot product graphs

In this paper, we study the change point localization problem in a seque...

0 Oscar Hernan Madrid Padilla, et al. ∙

research

∙ 11/07/2019

Improving Power of 2-Sample Random Graph Tests with Applications in Connectomics

In many applications, there is an interest in testing whether two graphs...

0 Bijan Varjavand, et al. ∙

research

∙ 10/20/2019

The Exact Equivalence of Independence Testing and Two-Sample Testing

Testing independence and testing equality of distributions are two tight...

0 Cencheng Shen, et al. ∙

research

∙ 09/29/2019

Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings

Graph embeddings, a class of dimensionality reduction techniques designe...

0 Keith Levin, et al. ∙

research

∙ 08/18/2019

Spectral inference for large Stochastic Blockmodels with nodal covariates

In many applications of network analysis, it is important to distinguish...

0 Angelo Mele, et al. ∙

Carey E. Priebe

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