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  4. Minimizing Regret in Unconstrained Online Convex Optimization
 
conference paper

Minimizing Regret in Unconstrained Online Convex Optimization

Tatarenko, Tatiana
•
Kamgarpour, Maryam  
June 2018
2018 European Control Conference (ECC)
2018 17th European Control Conference (ECC)

We consider online convex optimizations in the bandit setting. The decision maker does not know the time- varying cost functions, or their gradients. At each time step, she observes the value of the cost function for her chosen action. The objective is to minimize the regret, that is, the difference between the sum of the costs she accumulates and that of the optimal action computable had she known the cost functions a priori. We present a novel algorithm in order to minimize the regret in an unconstrained action space. Our algorithm hinges on the idea of introducing randomization to approximate the gradients of the cost functions using only their observed values. We establish an almost sure regret bound for the mean values of actions and an expected regret bound for the actions.

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Type
conference paper
DOI
10.23919/ECC.2018.8550310
Author(s)
Tatarenko, Tatiana
Kamgarpour, Maryam  
Date Issued

2018-06

Publisher

IEEE

Publisher place

Limassol

Published in
2018 European Control Conference (ECC)
ISBN of the book

978-3-9524269-8-2

Start page

143

End page

148

Editorial or Peer reviewed

REVIEWED

Written at

OTHER

EPFL units
SYCAMORE  
Event nameEvent placeEvent date
2018 17th European Control Conference (ECC)

Limassol

2018-06

Available on Infoscience
December 1, 2021
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/183338
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