Search Results for author: Venkat Chandrasekaran

Found 10 papers, 1 papers with code

Spectrahedral Regression

no code implementations27 Oct 2021 Eliza O'Reilly, Venkat Chandrasekaran

Convex regression is the problem of fitting a convex function to a data set consisting of input-output pairs.

Learning Exponential Family Graphical Models with Latent Variables using Regularized Conditional Likelihood

no code implementations19 Oct 2020 Armeen Taeb, Parikshit Shah, Venkat Chandrasekaran

Fitting a graphical model to a collection of random variables given sample observations is a challenging task if the observed variables are influenced by latent variables, which can induce significant confounding statistical dependencies among the observed variables.

A Note on Convex Relaxations for the Inverse Eigenvalue Problem

no code implementations6 Nov 2019 Utkan Candogan, Yong Sheng Soh, Venkat Chandrasekaran

The affine inverse eigenvalue problem consists of identifying a real symmetric matrix with a prescribed set of eigenvalues in an affine space.

Optimization and Control 15A18, 15A29, 90C22

Fitting Tractable Convex Sets to Support Function Evaluations

no code implementations11 Mar 2019 Yong Sheng Soh, Venkat Chandrasekaran

Our numerical experiments highlight the utility of our framework over previous approaches in settings in which the measurements available are noisy or small in number as well as those in which the underlying set to be reconstructed is non-polyhedral.

Statistics Theory Computational Geometry Optimization and Control Statistics Theory

Newton Polytopes and Relative Entropy Optimization

no code implementations3 Oct 2018 Riley Murray, Venkat Chandrasekaran, Adam Wierman

When specialized to the context of polynomials, we obtain analysis and computational tools that only depend on the particular monomials that constitute a sparse polynomial.

Optimization and Control

Learning Semidefinite Regularizers

no code implementations5 Jan 2017 Yong Sheng Soh, Venkat Chandrasekaran

The regularizers obtained using our framework can be employed effectively in semidefinite programming relaxations for solving inverse problems.

Dictionary Learning

High-Dimensional Change-Point Estimation: Combining Filtering with Convex Optimization

no code implementations11 Dec 2014 Yong Sheng Soh, Venkat Chandrasekaran

We consider change-point estimation in a sequence of high-dimensional signals given noisy observations.

Statistics Theory Information Theory Information Theory Optimization and Control Statistics Theory

Relative Entropy Relaxations for Signomial Optimization

1 code implementation26 Sep 2014 Venkat Chandrasekaran, Parikshit Shah

This sequence of lower bounds is computed by solving increasingly larger-sized relative entropy optimization problems, which are convex programs specified in terms of linear and relative entropy functions.

Optimization and Control

Recovery of Sparse Probability Measures via Convex Programming

no code implementations NeurIPS 2012 Mert Pilanci, Laurent E. Ghaoui, Venkat Chandrasekaran

We propose a direct relaxation of the minimum cardinality problem and show that it can be efficiently solved using convex programming.

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