11 accepted papers
Neural tangent kernels (NTKs) provide a theoretical regime to analyze the learning and generalization behavior of over-parametrized neural networks. For a supervised learning task, the association between the eigenvectors of the NTK and given data (a concept referred to as alignment in this paper) c…
In computational neuroscience, there has been an increased interest in developing machine learning algorithms that leverage brain imaging data to provide estimates of "brain age" for an individual. Importantly, the discordance between brain age and chronological age (referred to as "brain age gap")…
The deviation between chronological age and biological age is a well-recognized biomarker associated with cognitive decline and neurodegeneration. Age-related and pathology-driven changes to brain structure are captured by various neuroimaging modalities. These datasets are characterized by high dim…
Datasets involving sequences of different types of events without meaningful time stamps are prevalent in many applications, for instance when extracted from textual corpora. We propose a family of models for such event sequences -- summary Markov models -- where the probability of observing an even…
Graph neural networks (GNN) are an effective framework that exploit inter-relationships within graph-structured data for learning. Principal component analysis (PCA) involves the projection of data on the eigenspace of the covariance matrix and draws similarities with the graph convolutional filters…
Probabilistic graphical models (PGMs) are effective for capturing the statistical dependencies in stochastic databases. In many domains (e.g., working with multimodal data), one faces multiple information layers that can be modeled by structurally similar PGMs. While learning the structures of PGMs…
This paper formalizes a graph-constrained group testing (GT) framework for isolating up to k defective items from a population of p. In contrast to traditional group testing approaches, an underlying graphical model imposes constraints on how the items can be grouped for testing. The existing theori…
This paper considers the problem of jointly recovering the structures of two graphical models with unknown edge structures. It is assumed that both graphs have the same number of nodes and a known subset of nodes have identical structures in both graphs. The classes of Ising models and Gaussian mode…
Graphical models encode the stochastic dependencies among random variables (RVs). The vertices represent the RVs, and the edges signify the conditional dependencies among the RVs. Structure learning is the process of inferring the edges by observing realizations of the RVs, and it has applications…
This paper considers the problem of distributed state estimation in an interconnected network, in which there is uncertainty in the true model. Such uncertainties are due to the possibility of disruptions or changes in the nominal model. The focus is on the setting in which the true network model be…
This paper considers the problem of parameter estimation in a network in which the stochastic model of its measurements can change due to disruption in an unknown subset of sensors. This uncertainty in the measurements model introduces a new dimension to the estimator design. On one hand, the estima…