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no code implementations • 27 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.

no code implementations • 19 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.

no code implementations • 6 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

no code implementations • 11 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

no code implementations • 3 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

no code implementations • 5 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.

no code implementations • 11 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

1 code implementation • 26 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

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.

no code implementations • NeurIPS 2007 • Venkat Chandrasekaran, Alan S. Willsky, Jason K. Johnson

We consider the estimation problem in Gaussian graphical models with arbitrary structure.

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