Search Results for author:
Found 83 papers, 28 papers with code
Continuous Multidimensional Scaling
Multidimensional scaling (MDS) is the act of embedding proximity information about a set of $n$ objects in $d$-dimensional Euclidean space.
A Statistical Turing Test for Generative Models
Implicit in these efforts is an assumption that the generation properties of a human are different from that of the machine.
Gotta match 'em all: Solution diversification in graph matching matched filters
We present a novel approach for finding multiple noisily embedded template graphs in a very large background graph.
Comparing Foundation Models using Data Kernels
Further, we demonstrate how our methodology can be extended to facilitate population level model comparison.
Semisupervised regression in latent structure networks on unknown manifolds
We assume that the latent position vectors lie on an unknown one-dimensional curve and are coupled with a response covariate via a regression model.
Discovering Communication Pattern Shifts in Large-Scale Labeled Networks using Encoder Embedding and Vertex Dynamics
Analyzing large-scale time-series network data, such as social media and email communications, poses a significant challenge in understanding social dynamics, detecting anomalies, and predicting trends.
Synergistic Graph Fusion via Encoder Embedding
In this paper, we introduce a novel approach called graph fusion embedding, designed for multi-graph embedding with shared vertex sets.
Approximately optimal domain adaptation with Fisher's Linear Discriminant
We propose a class of models based on Fisher's Linear Discriminant (FLD) in the context of domain adaptation.
Graph Encoder Ensemble for Simultaneous Vertex Embedding and Community Detection
In this paper, we introduce a novel and computationally efficient method for vertex embedding, community detection, and community size determination.
Deep Learning is Provably Robust to Symmetric Label Noise
We conjecture that for general label noise, mitigation strategies that make use of the noisy data will outperform those that ignore the noisy data.
From Local to Global: Spectral-Inspired Graph Neural Networks
Graph Neural Networks (GNNs) are powerful deep learning methods for Non-Euclidean data.
Dynamic Network Sampling for Community Detection
We propose a dynamic network sampling scheme to optimize block recovery for stochastic blockmodel (SBM) in the case where it is prohibitively expensive to observe the entire graph.
The Value of Out-of-Distribution Data
In this work, we show a counter-intuitive phenomenon: the generalization error of a task can be a non-monotonic function of the number of OOD samples.
Deep Learning with Label Noise: A Hierarchical Approach
Our hierarchical approach improves upon regular deep neural networks in learning with label noise.
ART-SS: An Adaptive Rejection Technique for Semi-Supervised restoration for adverse weather-affected images
Although various weather degradation synthesis methods exist in the literature, the use of synthetically generated weather degraded images often results in sub-optimal performance on the real weather degraded images due to the domain gap between synthetic and real-world images.
Mental State Classification Using Multi-graph Features
We consider the problem of extracting features from passive, multi-channel electroencephalogram (EEG) devices for downstream inference tasks related to high-level mental states such as stress and cognitive load.
Prospective Learning: Principled Extrapolation to the Future
We conjecture that certain sequences of tasks are not retrospectively learnable (in which the data distribution is fixed), but are prospectively learnable (in which distributions may be dynamic), suggesting that prospective learning is more difficult in kind than retrospective learning.
Graph Matching via Optimal Transport
The graph matching problem seeks to find an alignment between the nodes of two graphs that minimizes the number of adjacency disagreements.
Towards a theory of out-of-distribution learning
This assumption renders these theories inadequate for characterizing 21$^{st}$ century real world data problems, which are typically characterized by evaluation distributions that differ from the training data distributions (referred to as out-of-distribution learning).
One-Hot Graph Encoder Embedding
In this paper we propose a lightning fast graph embedding method called one-hot graph encoder embedding.
When are Deep Networks really better than Decision Forests at small sample sizes, and how?
Empirically, we compare these two strategies on hundreds of tabular data settings, as well as several vision and auditory settings.
Leveraging semantically similar queries for ranking via combining representations
In modern ranking problems, different and disparate representations of the items to be ranked are often available.
Dynamic Silos: Increased Modularity in Intra-organizational Communication Networks during the Covid-19 Pandemic
Workplace communications around the world were drastically altered by Covid-19, related work-from-home orders, and the rise of remote work.
Inducing a hierarchy for multi-class classification problems
In applications where categorical labels follow a natural hierarchy, classification methods that exploit the label structure often outperform those that do not.
Subgraph nomination: Query by Example Subgraph Retrieval in Networks
This paper introduces the subgraph nomination inference task, in which example subgraphs of interest are used to query a network for similarly interesting subgraphs.
Discovery of Multi-Level Network Differences Across Populations of Heterogeneous Connectomes
A connectome is a map of the structural and/or functional connections in the brain.
A partition-based similarity for classification distributions
Herein we define a measure of similarity between classification distributions that is both principled from the perspective of statistical pattern recognition and useful from the perspective of machine learning practitioners.
A Simple Spectral Failure Mode for Graph Convolutional Networks
Neural networks have achieved remarkable successes in machine learning tasks.
Multiple Network Embedding for Anomaly Detection in Time Series of Graphs
We examine two related, complementary inference tasks: the detection of anomalous graphs within a time series, and the detection of temporally anomalous vertices.
Methodology
On spectral algorithms for community detection in stochastic blockmodel graphs with vertex covariates
In network inference applications, it is often desirable to detect community structure, namely to cluster vertices into groups, or blocks, according to some measure of similarity.
Distance-based Positive and Unlabeled Learning for Ranking
Learning to rank -- producing a ranked list of items specific to a query and with respect to a set of supervisory items -- is a problem of general interest.
Vertex Nomination in Richly Attributed Networks
In this paper, we explore, both theoretically and practically, the dual roles of content (i. e., edge and vertex attributes) and context (i. e., network topology) in vertex nomination.
Omnidirectional Transfer for Quasilinear Lifelong Learning
But striving to avoid forgetting sets the goal unnecessarily low: the goal of lifelong learning, whether biological or artificial, should be to improve performance on all tasks (including past and future) with any new data.
Learning 1-Dimensional Submanifolds for Subsequent Inference on Random Dot Product Graphs
We submit that techniques for manifold learning can be used to learn the unknown submanifold well enough to realize benefit from restricted inference.
On Two Distinct Sources of Nonidentifiability in Latent Position Random Graph Models
Two separate and distinct sources of nonidentifiability arise naturally in the context of latent position random graph models, though neither are unique to this setting.
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 in order to maximize the shared structure across networks; when the graphs are unipartite, this is commonly formulated as minimizing their edge disagreements.
LqRT: Robust Hypothesis Testing of Location Parameters using Lq-Likelihood-Ratio-Type Test in Python
To the extent that the robustness of the Wilcoxon test (minimum asymptotic relative efficiency (ARE) of the Wilcoxon test vs the t-test is 0. 864) suggests that the Wilcoxon test should be the default test of choice (rather than "use Wilcoxon if there is evidence of non-normality", the default position should be "use Wilcoxon unless there is good reason to believe the normality assumption"), the results in this article suggest that the LqRT is potentially the new default go-to test for practitioners.
Methodology
High-dimensional and universally consistent k-sample tests
The evaluation included several popular independence statistics and covered a comprehensive set of simulations.
Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings
In both cases, we prove that when the underlying graph is generated according to a latent space model called the random dot product graph, which includes the popular stochastic block model as a special case, an out-of-sample extension based on a least-squares objective obeys a central limit theorem about the true latent position of the out-of-sample vertex.
Spectral inference for large Stochastic Blockmodels with nodal covariates
In many applications of network analysis, it is important to distinguish between observed and unobserved factors affecting network structure.
Graphyti: A Semi-External Memory Graph Library for FlashGraph
Emerging frameworks avoid the network bottleneck of distributed data with Semi-External Memory (SEM) that uses a single multicore node and operates on graphs larger than memory.
Distributed, Parallel, and Cluster Computing Databases
Geodesic Learning via Unsupervised Decision Forests
Empirical results on simulated and real data demonstrate that URerF is robust to high-dimensional noise, where as other methods, such as Isomap, UMAP, and FLANN, quickly deteriorate in such settings.
Vertex Nomination, Consistent Estimation, and Adversarial Modification
Given a pair of graphs $G_1$ and $G_2$ and a vertex set of interest in $G_1$, the vertex nomination (VN) problem seeks to find the corresponding vertices of interest in $G_2$ (if they exist) and produce a rank list of the vertices in $G_2$, with the corresponding vertices of interest in $G_2$ concentrating, ideally, at the top of the rank list.
Simultaneous Dimensionality and Complexity Model Selection for Spectral Graph Clustering
The second contribution is a simultaneous model selection framework.
Maximum Likelihood Estimation and Graph Matching in Errorfully Observed Networks
Given a pair of graphs with the same number of vertices, the inexact graph matching problem consists in finding a correspondence between the vertices of these graphs that minimizes the total number of induced edge disagreements.
On a 'Two Truths' Phenomenon in Spectral Graph Clustering
Clustering is concerned with coherently grouping observations without any explicit concept of true groupings.
Matched Filters for Noisy Induced Subgraph Detection
To illustrate the possibilities and challenges of such problems, we use an algorithm that can exploit a partially known correspondence and show via varied simulations and applications to {\it Drosophila} and human connectomes that this approach can achieve good performance.
Out-of-sample extension of graph adjacency spectral embedding
Many popular dimensionality reduction procedures have out-of-sample extensions, which allow a practitioner to apply a learned embedding to observations not seen in the initial training sample.
On consistent vertex nomination schemes
Given a vertex of interest in a network $G_1$, the vertex nomination problem seeks to find the corresponding vertex of interest (if it exists) in a second network $G_2$.
From Distance Correlation to Multiscale Graph Correlation
Understanding and developing a correlation measure that can detect general dependencies is not only imperative to statistics and machine learning, but also crucial to general scientific discovery in the big data age.
Statistical inference on random dot product graphs: a survey
In this survey paper, we describe a comprehensive paradigm for statistical inference on random dot product graphs, a paradigm centered on spectral embeddings of adjacency and Laplacian matrices.
A statistical interpretation of spectral embedding: the generalised random dot product graph
Spectral embedding is a procedure which can be used to obtain vector representations of the nodes of a graph.
Semiparametric spectral modeling of the Drosophila connectome
We present semiparametric spectral modeling of the complete larval Drosophila mushroom body connectome.
Vertex Nomination Via Seeded Graph Matching
Consider two networks on overlapping, non-identical vertex sets.
Joint Embedding of Graphs
Feature extraction and dimension reduction for networks is critical in a wide variety of domains.
Statistical inference for network samples using subgraph counts
Our results yield joint confidence regions for subgraph counts, and therefore methods for testing whether the observations in a network sample are drawn from: a specified distribution, a specified model, or from the same model as another network sample.
Methodology Social and Information Networks 62G05, 05C80, 62G10
Discovering and Deciphering Relationships Across Disparate Data Modalities
Understanding the relationships between different properties of data, such as whether a connectome or genome has information about disease status, is becoming increasingly important in modern biological datasets.
Connectome Smoothing via Low-rank Approximations
In statistical connectomics, the quantitative study of brain networks, estimating the mean of a population of graphs based on a sample is a core problem.
Limit theorems for eigenvectors of the normalized Laplacian for random graphs
As a corollary, we show that for stochastic blockmodel graphs, the rows of the spectral embedding of the normalized Laplacian converge to multivariate normals and furthermore the mean and the covariance matrix of each row are functions of the associated vertex's block membership.
On the Consistency of the Likelihood Maximization Vertex Nomination Scheme: Bridging the Gap Between Maximum Likelihood Estimation and Graph Matching
Given a graph in which a few vertices are deemed interesting a priori, the vertex nomination task is to order the remaining vertices into a nomination list such that there is a concentration of interesting vertices at the top of the list.
knor: A NUMA-Optimized In-Memory, Distributed and Semi-External-Memory k-means Library
The \textit{k-means NUMA Optimized Routine} (\textsf{knor}) library has (i) in-memory (\textsf{knori}), (ii) distributed memory (\textsf{knord}), and (iii) semi-external memory (\textsf{knors}) modules that radically improve the performance of k-means for varying memory and hardware budgets.
Distributed, Parallel, and Cluster Computing
FlashR: R-Programmed Parallel and Scalable Machine Learning using SSDs
R is one of the most popular programming languages for statistics and machine learning, but the R framework is relatively slow and unable to scale to large datasets.
Distributed, Parallel, and Cluster Computing
Semi-External Memory Sparse Matrix Multiplication for Billion-Node Graphs
In contrast, we scale sparse matrix multiplication beyond memory capacity by implementing sparse matrix dense matrix multiplication (SpMM) in a semi-external memory (SEM) fashion; i. e., we keep the sparse matrix on commodity SSDs and dense matrices in memory.
Distributed, Parallel, and Cluster Computing
Semi-supervised K-means++
Traditionally, practitioners initialize the {\tt k-means} algorithm with centers chosen uniformly at random.
Sparse Projection Oblique Randomer Forests
Unfortunately, these extensions forfeit one or more of the favorable properties of decision forests based on axis-aligned splits, such as robustness to many noise dimensions, interpretability, or computational efficiency.
Community Detection and Classification in Hierarchical Stochastic Blockmodels
We propose a robust, scalable, integrated methodology for community detection and community comparison in graphs.
Fast Embedding for JOFC Using the Raw Stress Criterion
The Joint Optimization of Fidelity and Commensurability (JOFC) manifold matching methodology embeds an omnibus dissimilarity matrix consisting of multiple dissimilarities on the same set of objects.
Sparse Representation Classification Beyond L1 Minimization and the Subspace Assumption
The results are demonstrated via simulations and real data experiments, where the new algorithm achieves comparable numerical performance and significantly faster.
Manifold Matching using Shortest-Path Distance and Joint Neighborhood Selection
Then the shortest-path distance within each modality is calculated from the joint neighborhood graph, followed by embedding into and matching in a common low-dimensional Euclidean space.
An Automated Images-to-Graphs Framework for High Resolution Connectomics
Finally, we deploy a reference end-to-end version of the pipeline on a large, publicly available data set.
Empirical Bayes Estimation for the Stochastic Blockmodel
Inference for the stochastic blockmodel is currently of burgeoning interest in the statistical community, as well as in various application domains as diverse as social networks, citation networks, brain connectivity networks (connectomics), etc.
Graph Matching: Relax at Your Own Risk
Indeed, experimental results illuminate and corroborate these theoretical findings, demonstrating that excellent results are achieved in both benchmark and real data problems by amalgamating the two approaches.
A model selection approach for clustering a multinomial sequence with non-negative factorization
We consider a problem of clustering a sequence of multinomial observations by way of a model selection criterion.
Spectral Clustering for Divide-and-Conquer Graph Matching
We present a parallelized bijective graph matching algorithm that leverages seeds and is designed to match very large graphs.
A central limit theorem for scaled eigenvectors of random dot product graphs
We prove a central limit theorem for the components of the largest eigenvectors of the adjacency matrix of a finite-dimensional random dot product graph whose true latent positions are unknown.
Out-of-sample Extension for Latent Position Graphs
We show that, under the latent position graph model and for sufficiently large $n$, the mapping of the out-of-sample vertices is close to its true latent position.
Generalized Canonical Correlation Analysis for Classification
For multiple multivariate data sets, we derive conditions under which Generalized Canonical Correlation Analysis (GCCA) improves classification performance of the projected datasets, compared to standard Canonical Correlation Analysis (CCA) using only two data sets.
On the Incommensurability Phenomenon
Suppose that two large, multi-dimensional data sets are each noisy measurements of the same underlying random process, and principle components analysis is performed separately on the data sets to reduce their dimensionality.
Universally consistent vertex classification for latent positions graphs
In this work we show that, using the eigen-decomposition of the adjacency matrix, we can consistently estimate feature maps for latent position graphs with positive definite link function $\kappa$, provided that the latent positions are i. i. d.
Statistical inference on errorfully observed graphs
Thus we errorfully observe $G$ when we observe the graph $\widetilde{G} = (V,\widetilde{E})$ as the edges in $\widetilde{E}$ arise from the classifications of the "edge-features", and are expected to be errorful.
Seeded Graph Matching
Given two graphs, the graph matching problem is to align the two vertex sets so as to minimize the number of adjacency disagreements between the two graphs.
On latent position inference from doubly stochastic messaging activities
Each of the message-exchanging actors is modeled as a process in a latent space.
