Many inferential tasks, such as analyzing the functional connectivity of the brain via coactivation patterns or capturing the changing correlations amongst a set of assets for portfolio optimization, rely on modeling a covariance matrix whose elements evolve as a function of time. A number of multivariate heteroscedastic time series models …
Sheida’s talk: In this research we analyzed effective and functional connectivity in a population of five subjects with spina bifida hydrocephalus (SBH) and five healthy control subjects using resting state magnetoencephalography (MEG) recordings. Three types of connectivity are used to describe the brain. Anatomical connectivity refers to physical connections between …
We will discuss a line of recent work on methods for statistical inference in high dimensions. In many real-world applications, samples are not collected cleanly and may be observed subject to systematic corruptions such as missing data and additive noise. We describe how Lasso-based linear regression may be corrected to …
Matt’s talk: Title: Sequential Testing in High Dimensions Sequential methods make use of information as it becomes available, creating an interactive connection between a sampling procedure and information gathered by that procedure. In this talk we explore sequential methods applied to sparse recovery problems, motivated by applications in both communications …
*** Hongbo’s talk: Title: Using a New Nonconvex Singular Value Regularizer in Multivariate Linear Regression. We introduce the weighted singlar value penalization, which uses a nonconvex nonseparable regularizer. We show that in spite of the nonconvexity, one optimization problem with this regularizer is efficiently solvable. Applied to Multivariate Linear Regression, …
***Bryan: Both cognitive constraints and limited amounts of data restrict the complexity of inferential models. Sparse coding is an elegant way to address these restrictions, extracting correlated subparts from data in a way that can be efficient, predictive, and adaptive. We present a sparse coding method for use on binary …
*** Pari’s talk: Title: Group Symmetry and Covariance Regularization Statistical models that possess symmetry arise in diverse settings such as random fields associated to geophysical phenomena, exchangeable processes in Bayesian statistics, and cyclostationary processes in engineering. We formalize the notion of a symmetric model via group invariance. We propose projection …
Transcriptional regulatory networks are networks of genes and transcription factor proteins that determine the context-specific expression pattern of a gene, where a context could represent a time point, a spatial location or a combination of these. Although these networks are a key to accurate information processing and function in living …
*** William’s talk: Title: Secretary Problems with Convex Cost We consider online resource allocation problems where given a set of requests our goal is to select a subset that maximizes a value minus cost type of objective function. Requests are presented online in random order, and each request possesses an …
*** Atul’s talk: Title: The Least-Squares Invertible Constant-Q Spectrogram (LSICQS) and Its Application to Phase Vocoding Building on the idea of a constant-Q transform popularized by Judith Brown in the 1990’s, we will develop a constant-Q spectrogram representation which is invertible in a least-squares sense. We will see that the …