Add to ORKGOptimal measurement selection for inference is combinatori-ally complex and intractable for large scale problems. Un-der mild technical conditions, it has been proven that greedy heuristics combined with conditional mutual information re-wards achieve performance within a factor of the optimal. Here we provide conditions under which cost-penalized mu-tual information may achieve similar guarantees. Specifically, if the cost of a measurement is proportional to the information it conveys, the bounds proven in [4] and [10] still apply. Index Terms — information measures, sensor selection 1
In distributed sensor networks, computational and energy resources are in general limited. Therefore...
We use the concept of conditional mutual information (MI) to approach problems involving the selecti...
Utility maximization is a key element of a number of theoretical approaches to explaining human beha...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Comp...
ABSTRACT. Computing value of information (VOI) is a crucial task in various aspects of decision-maki...
Submodular function maximization finds application in a variety of real-world decision-making proble...
Abstract—The purpose of this article is to examine the greedy adaptive measurement policy in the con...
Optimal information gathering is a central challenge in machine learning and science in general. A c...
Submodular function maximization finds application in a variety of real-world decision-making proble...
Information theoretic criteria (ITC) have been widely adopted in engineering and statistics for sele...
We consider the Bayesian ranking and selection problem, in which one wishes to allocate an informati...
In many environments it is costly for decision makers to determine which option is best for them bec...
We address the following sensor selection problem. We assume that a dynamic system possesses a certa...
This paper presents an information-theoretic framework for the optimal selection of sensors across a...
We take another look at the general problem of selecting a preferred probability measure among tho...
In distributed sensor networks, computational and energy resources are in general limited. Therefore...
We use the concept of conditional mutual information (MI) to approach problems involving the selecti...
Utility maximization is a key element of a number of theoretical approaches to explaining human beha...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Comp...
ABSTRACT. Computing value of information (VOI) is a crucial task in various aspects of decision-maki...
Submodular function maximization finds application in a variety of real-world decision-making proble...
Abstract—The purpose of this article is to examine the greedy adaptive measurement policy in the con...
Optimal information gathering is a central challenge in machine learning and science in general. A c...
Submodular function maximization finds application in a variety of real-world decision-making proble...
Information theoretic criteria (ITC) have been widely adopted in engineering and statistics for sele...
We consider the Bayesian ranking and selection problem, in which one wishes to allocate an informati...
In many environments it is costly for decision makers to determine which option is best for them bec...
We address the following sensor selection problem. We assume that a dynamic system possesses a certa...
This paper presents an information-theoretic framework for the optimal selection of sensors across a...
We take another look at the general problem of selecting a preferred probability measure among tho...
In distributed sensor networks, computational and energy resources are in general limited. Therefore...
We use the concept of conditional mutual information (MI) to approach problems involving the selecti...
Utility maximization is a key element of a number of theoretical approaches to explaining human beha...