TY - GEN
T1 - Gaussian process optimization with mutual information
AU - Contal, Emile
AU - Perchet, Vianney
AU - Vayatis, Nicolas
N1 - Publisher Copyright:
Copyright © (2014) by the International Machine Learning Society (IMLS) All rights reserved.
PY - 2014/1/1
Y1 - 2014/1/1
N2 - In this paper, we analyze a generic algorithm scheme for sequential global optimization using Gaussian processes. The upper bounds we derive on the cumulative regret for this generic algo-rithm improve by an exponential factor the previously known bounds for algorithms like GP- UCB. We also introduce the novel Gaussian Process Mutual Information algorithm (GP-MI), which significantly improves further these upper bounds for the cumulative regret. We confirm the efficiency of this algorithm on synthetic and real tasks against the natural competitor, GP-UCB, and also the Expected Improvement heuristic.
AB - In this paper, we analyze a generic algorithm scheme for sequential global optimization using Gaussian processes. The upper bounds we derive on the cumulative regret for this generic algo-rithm improve by an exponential factor the previously known bounds for algorithms like GP- UCB. We also introduce the novel Gaussian Process Mutual Information algorithm (GP-MI), which significantly improves further these upper bounds for the cumulative regret. We confirm the efficiency of this algorithm on synthetic and real tasks against the natural competitor, GP-UCB, and also the Expected Improvement heuristic.
M3 - Conference contribution
AN - SCOPUS:84919880069
T3 - 31st International Conference on Machine Learning, ICML 2014
SP - 1515
EP - 1523
BT - 31st International Conference on Machine Learning, ICML 2014
PB - International Machine Learning Society (IMLS)
T2 - 31st International Conference on Machine Learning, ICML 2014
Y2 - 21 June 2014 through 26 June 2014
ER -