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Found 12 papers, 1 papers with code
Relative Entropy Relaxations for Signomial Optimization
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
Learning Semidefinite Regularizers
The regularizers obtained using our framework can be employed effectively in semidefinite programming relaxations for solving inverse problems.
Newton Polytopes and Relative Entropy Optimization
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
Recovery of Sparse Probability Measures via Convex Programming
We propose a direct relaxation of the minimum cardinality problem and show that it can be efficiently solved using convex programming.
Adaptive Embedded Subgraph Algorithms using Walk-Sum Analysis
We consider the estimation problem in Gaussian graphical models with arbitrary structure.
Fitting Tractable Convex Sets to Support Function Evaluations
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
High-Dimensional Change-Point Estimation: Combining Filtering with Convex Optimization
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
A Note on Convex Relaxations for the Inverse Eigenvalue Problem
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
Learning Exponential Family Graphical Models with Latent Variables using Regularized Conditional Likelihood
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.
Spectrahedral Regression
Convex regression is the problem of fitting a convex function to a data set consisting of input-output pairs.
Optimal Regularization for a Data Source
In this paper, we seek a systematic understanding of the power and the limitations of convex regularization by investigating the following questions: Given a distribution, what is the optimal regularizer for data drawn from the distribution?
Modeling groundwater levels in California's Central Valley by hierarchical Gaussian process and neural network regression
Modeling groundwater levels continuously across California's Central Valley (CV) hydrological system is challenging due to low-quality well data which is sparsely and noisily sampled across time and space.
