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09/16/2023
A Statistical Turing Test for Generative Models
The emergence of human-like abilities of AI systems for content generati...
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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...
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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...
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05/09/2023
Comparing Foundation Models using Data Kernels
Recent advances in self-supervised learning and neural network scaling h...
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05/04/2023
Semisupervised regression in latent structure networks on unknown manifolds
Random graphs are increasingly becoming objects of interest for modeling...
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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...
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04/18/2023
Independence testing for inhomogeneous random graphs
Testing for independence between graphs is a problem that arises natural...
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03/31/2023
Synergistic Graph Fusion via Encoder Embedding
In this paper, we introduce a novel approach to multi-graph embedding ca...
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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)...
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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...
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10/26/2022
Deep Learning is Provably Robust to Symmetric Label Noise
Deep neural networks (DNNs) are capable of perfectly fitting the trainin...
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09/24/2022
From Local to Global: Spectral-Inspired Graph Neural Networks
Graph Neural Networks (GNNs) are powerful deep learning methods for Non-...
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08/29/2022
Dynamic Network Sampling for Community Detection
We propose a dynamic network sampling scheme to optimize block recovery ...
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08/23/2022
The Value of Out-of-Distribution Data
More data helps us generalize to a task. But real datasets can contain o...
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05/28/2022
Deep Learning with Label Noise: A Hierarchical Approach
Deep neural networks are susceptible to label noise. Existing methods to...
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05/13/2022
Discovering underlying dynamics in time series of networks
Understanding dramatic changes in the evolution of networks is central t...
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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...
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02/25/2022
Mental State Classification Using Multi-graph Features
We consider the problem of extracting features from passive, multi-chann...
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01/19/2022
Prospective Learning: Back to the Future
Research on both natural intelligence (NI) and artificial intelligence (...
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11/09/2021
Graph Matching via Optimal Transport
The graph matching problem seeks to find an alignment between the nodes ...
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09/29/2021
Towards a theory of out-of-distribution learning
What is learning? 20^st century formalizations of learning theory – whic...
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09/27/2021
Graph Encoder Embedding
In this paper we propose a lightning fast graph embedding method called ...
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07/23/2021
A principled (and practical) test for network comparison
How might one test the hypothesis that graphs were sampled from the same...
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06/23/2021
Leveraging semantically similar queries for ranking via combining representations
In modern ranking problems, different and disparate representations of t...
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05/04/2021
Occam Factor for Gaussian Models With Unknown Variance Structure
We discuss model selection to determine whether the variance-covariance ...
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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...
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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 ...
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02/20/2021
Inducing a hierarchy for multi-class classification problems
In applications where categorical labels follow a natural hierarchy, cla...
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01/29/2021
Subgraph nomination: Query by Example Subgraph Retrieval in Networks
This paper introduces the subgraph nomination inference task, in which e...
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11/12/2020
A partition-based similarity for classification distributions
Herein we define a measure of similarity between classification distribu...
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10/25/2020
A Simple Spectral Failure Mode for Graph Convolutional Networks
We present a simple generative model in which spectral graph embedding f...
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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...
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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...
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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...
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07/04/2020
On identifying unobserved heterogeneity in stochastic blockmodel graphs with vertex covariates
Both observed and unobserved vertex heterogeneity can influence block st...
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05/20/2020
Learning to rank via combining representations
Learning to rank – producing a ranked list of items specific to a query ...
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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 ...
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04/27/2020
A general approach to progressive intelligence
In biological learning, data is used to improve performance on the task ...
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04/27/2020
A general approach to progressive learning
In biological learning, data is used to improve performance on the task ...
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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 ...
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03/31/2020
On Two Distinct Sources of Nonidentifiability in Latent Position Random Graph Models
Two separate and distinct sources of nonidentifiability arise naturally ...
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03/30/2020
Spectral graph clustering via the Expectation-Solution algorithm
The stochastic blockmodel (SBM) models the connectivity within and betwe...
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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 ...
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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...
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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 ...
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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...
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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...
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10/20/2019
The Exact Equivalence of Independence Testing and Two-Sample Testing
Testing independence and testing equality of distributions are two tight...
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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...
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08/18/2019