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Found 44 papers, 3 papers with code
Community Detection in High-Dimensional Graph Ensembles
Detecting communities in high-dimensional graphs can be achieved by applying random matrix theory where the adjacency matrix of the graph is modeled by a Stochastic Block Model (SBM).
Iterative Sketching for Secure Coded Regression
Linear regression is a fundamental and primitive problem in supervised machine learning, with applications ranging from epidemiology to finance.
SOLBP: Second-Order Loopy Belief Propagation for Inference in Uncertain Bayesian Networks
In second-order uncertain Bayesian networks, the conditional probabilities are only known within distributions, i. e., probabilities over probabilities.
Uncertain Bayesian Networks: Learning from Incomplete Data
When the historical data are limited, the conditional probabilities associated with the nodes of Bayesian networks are uncertain and can be empirically estimated.
High dimensional stochastic linear contextual bandit with missing covariates
Recent works in bandit problems adopted lasso convergence theory in the sequential decision-making setting.
Data Discovery Using Lossless Compression-Based Sparse Representation
Sparse representation has been widely used in data compression, signal and image denoising, dimensionality reduction and computer vision.
Space-Time Adaptive Detection at Low Sample Support
An important problem in space-time adaptive detection is the estimation of the large p-by-p interference covariance matrix from training signals.
The Power of Graph Convolutional Networks to Distinguish Random Graph Models: Short Version
We investigate the power of GCNs, as a function of their number of layers, to distinguish between different random graph models on the basis of the embeddings of their sample graphs.
Fundamental Limits of Deep Graph Convolutional Networks
We give a precise characterization of the set of pairs of graphons that are indistinguishable by a GCN with nonlinear activation functions coming from a certain broad class if its depth is at least logarithmic in the size of the sample graph.
Semi-supervised Learning in Network-Structured Data via Total Variation Minimization
This lends naturally to learning the labels by total variation (TV) minimization, which we solve by applying a recently proposed primal-dual method for non-smooth convex optimization.
Scalable Mutual Information Estimation using Dependence Graphs
To the best of our knowledge EDGE is the first non-parametric MI estimator that can achieve parametric MSE rates with linear time complexity.
Semiblind subgraph reconstruction in Gaussian graphical models
Consider a social network where only a few nodes (agents) have meaningful interactions in the sense that the conditional dependency graph over node attribute variables (behaviors) is sparse.
Rate-optimal Meta Learning of Classification Error
Meta learning of optimal classifier error rates allows an experimenter to empirically estimate the intrinsic ability of any estimator to discriminate between two populations, circumventing the difficult problem of estimating the optimal Bayes classifier.
Direct Estimation of Information Divergence Using Nearest Neighbor Ratios
Considering the $k$-nearest neighbor ($k$-NN) graph of $Y$ in the joint data set $(X, Y)$, we show that the average powered ratio of the number of $X$ points to the number of $Y$ points among all $k$-NN points is proportional to R\'{e}nyi divergence of $X$ and $Y$ densities.
Similarity Function Tracking using Pairwise Comparisons
We propose Online Convex Ensemble StrongLy Adaptive Dynamic Learning (OCELAD), a general adaptive, online approach for learning and tracking optimal metrics as they change over time that is highly robust to a variety of nonstationary behaviors in the changing metric.
Semi-Supervised Learning via Sparse Label Propagation
This learning algorithm allows for a highly scalable implementation as message passing over the underlying data graph.
Scalable Semi-Supervised Learning over Networks using Nonsmooth Convex Optimization
We propose a scalable method for semi-supervised (transductive) learning from massive network-structured datasets.
Multilayer Spectral Graph Clustering via Convex Layer Aggregation
Multilayer graphs are commonly used for representing different relations between entities and handling heterogeneous data processing tasks.
AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering
One of the longstanding problems in spectral graph clustering (SGC) is the so-called model order selection problem: automated selection of the correct number of clusters.
Information Theoretic Structure Learning with Confidence
Information theoretic measures (e. g. the Kullback Liebler divergence and Shannon mutual information) have been used for exploring possibly nonlinear multivariate dependencies in high dimension.
Learning Sparse Graphs Under Smoothness Prior
Given the noisy data, we show that the joint sparse graph learning and denoising problem can be simplified to designing only the sparse edge selection vector, which can be solved using convex optimization.
The intrinsic value of HFO features as a biomarker of epileptic activity
High frequency oscillations (HFOs) are a promising biomarker of epileptic brain tissue and activity.
Multi-criteria Similarity-based Anomaly Detection using Pareto Depth Analysis
If the relative importance of the different dissimilarity measures are not known in advance, as in many anomaly detection applications, the anomaly detection algorithm may need to be executed multiple times with different choices of weights in the linear combination.
Semi-supervised Multi-sensor Classification via Consensus-based Multi-View Maximum Entropy Discrimination
In this paper, we consider multi-sensor classification when there is a large number of unlabeled samples.
Meta learning of bounds on the Bayes classifier error
For example, the Bayes error rate of a given feature space, if known, can be used to aid in choosing a classifier, as well as in feature selection and model selection for the base classifiers and the meta classifier.
Image patch analysis of sunspots and active regions. II. Clustering via matrix factorization
We also find that including data focused on the neutral line of an active region can result in an increased correspondence between our clustering results and other active region descriptors such as the Mount Wilson classifications and the $R$ value.
Phase Transitions in Spectral Community Detection of Large Noisy Networks
We prove phase transitions in community detectability as a function of the external edge connection probability and the noisy edge presence probability under a general network model where two arbitrarily connected communities are interconnected by random external edges.
Image patch analysis of sunspots and active regions. I. Intrinsic dimension and correlation analysis
We find the relationship between complexity of an active region as measured by Mount Wilson and the intrinsic dimension of its image patches.
Multivariate f-Divergence Estimation With Confidence
The problem of f-divergence estimation is important in the fields of machine learning, information theory, and statistics.
MIST: L0 Sparse Linear Regression with Momentum
Significant attention has been given to minimizing a penalized least squares criterion for estimating sparse solutions to large linear systems of equations.
L0 Sparse Inverse Covariance Estimation
In this paper we consider non-convex $l_0$ penalized log-likelihood inverse covariance estimation and present a novel cyclic descent algorithm for its optimization.
Image patch analysis and clustering of sunspots: a dimensionality reduction approach
Sunspots, as seen in white light or continuum images, are associated with regions of high magnetic activity on the Sun, visible on magnetogram images.
Learning Latent Variable Gaussian Graphical Models
Gaussian graphical models (GGM) have been widely used in many high-dimensional applications ranging from biological and financial data to recommender systems.
Kronecker PCA Based Spatio-Temporal Modeling of Video for Dismount Classification
We consider the application of KronPCA spatio-temporal modeling techniques [Greenewald et al 2013, Tsiligkaridis et al 2013] to the extraction of spatiotemporal features for video dismount classification.
Spectral Correlation Hub Screening of Multivariate Time Series
This property permits independent correlation analysis at each frequency, alleviating the computational and statistical challenges of high-dimensional time series.
Dynamic stochastic blockmodels for time-evolving social networks
There has been recent interest in statistical modeling of dynamic networks, which are observed at multiple points in time and offer a richer representation of many complex phenomena.
Pareto-depth for Multiple-query Image Retrieval
Most content-based image retrieval systems consider either one single query, or multiple queries that include the same object or represent the same semantic information.
Nonlinear unmixing of hyperspectral images using a semiparametric model and spatial regularization
Incorporating spatial information into hyperspectral unmixing procedures has been shown to have positive effects, due to the inherent spatial-spectral duality in hyperspectral scenes.
Kronecker Sum Decompositions of Space-Time Data
To allow a smooth tradeoff between the reduction in the number of parameters (to reduce estimation variance) and the accuracy of the covariance approximation (affecting estimation bias), we introduce a diagonally loaded modification of the sum of kronecker products representation [1].
Revealing social networks of spammers through spectral clustering
To date, most studies on spam have focused only on the spamming phase of the spam cycle and have ignored the harvesting phase, which consists of the mass acquisition of email addresses.
Dynamic stochastic blockmodels: Statistical models for time-evolving networks
Significant efforts have gone into the development of statistical models for analyzing data in the form of networks, such as social networks.
Marginal Likelihoods for Distributed Parameter Estimation of Gaussian Graphical Models
In this paper, we propose a general framework for distributed estimation based on a maximum marginal likelihood (MML) approach.
Covariance Estimation in High Dimensions via Kronecker Product Expansions
We show that a class of block Toeplitz covariance matrices is approximatable by low separation rank and give bounds on the minimal separation rank $r$ that ensures a given level of bias.
Convergence Properties of Kronecker Graphical Lasso Algorithms
The KGlasso algorithm generalizes Glasso, introduced by Yuan and Lin ["Model selection and estimation in the Gaussian graphical model," Biometrika, vol.
