@inproceedings{5e266dd9e4bc43fc8b3a0db41b822c30,
title = "Data-driven prediction of EVAR with confidence in time-varying datasets",
abstract = "The key challenge for learning-based autonomous systems operating in time-varying environments is to predict when the learned model may lose relevance. If the learned model loses relevance, then the autonomous system is at risk of making wrong decisions. The entropic value at risk (EVAR) is a computationally efficient and coherent risk measure that can be utilized to quantify this risk. In this paper, we present a Bayesian model and learning algorithms to predict the state-dependent EVAR of time-varying datasets. We discuss applications of EVAR to an exploration problem in which an autonomous agent has to choose a set of sensing locations in order to maximize the informativeness of the acquired data and learn a model of an underlying phenomenon of interest. We empirically demonstrate the efficacy of the presented model and learning algorithms on four real-world datasets.",
author = "Allan Axelrod and Luca Carlone and Girish Chowdhary and Sertac Karaman",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 55th IEEE Conference on Decision and Control, CDC 2016 ; Conference date: 12-12-2016 Through 14-12-2016",
year = "2016",
month = dec,
day = "27",
doi = "10.1109/CDC.2016.7799166",
language = "English (US)",
series = "2016 IEEE 55th Conference on Decision and Control, CDC 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5833--5838",
booktitle = "2016 IEEE 55th Conference on Decision and Control, CDC 2016",
address = "United States",
}