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Optimization experiments in the continuous space: The limited growth optimistic optimization algorithm

Issa Mattos, David, 1990 (författare)
Chalmers tekniska högskola,Chalmers University of Technology,Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, Göteborg, 412 96, Sweden
Mårtensson, Erling (författare)
Sony Mobile Communications Inc.,Sony Mobile Communications, Nya Vattentornet, Lund, 221 88, Sweden
Bosch, Jan, 1967 (författare)
Chalmers tekniska högskola,Chalmers University of Technology,Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, Göteborg, 412 96, Sweden
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Olsson Holmström, Helena (författare)
Malmö universitet,Institutionen för datavetenskap och medieteknik (DVMT)
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 (creator_code:org_t)
2018-08-22
2018
Engelska.
Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. ; 11036 LNCS, s. 293-308, s. 293-308
  • Konferensbidrag (refereegranskat)
Abstract Ämnesord
Stäng  
  • Online controlled experiments are extensively used by web-facing companies to validate and optimize their systems, providing a competitive advantage in their business. As the number of experiments scale, companies aim to invest their experimentation resources in larger feature changes and leave the automated techniques to optimize smaller features. Optimization experiments in the continuous space are encompassed in the many-armed bandits class of problems. Although previous research provides algorithms for solving this class of problems, these algorithms were not implemented in real-world online experimentation problems and do not consider the application constraints, such as time to compute a solution, selection of a best arm and the estimation of the mean-reward function. This work discusses the online experiments in context of the many-armed bandits class of problems and provides three main contributions: (1) an algorithm modification to include online experiments constraints, (2) implementation of this algorithm in an industrial setting in collaboration with Sony Mobile, and (3) statistical evidence that supports the modification of the algorithm for online experiments scenarios. These contributions support the relevance of the LG-HOO algorithm in the context of optimization experiments and show how the algorithm can be used to support continuous optimization of online systems in stochastic scenarios.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)

Nyckelord

Online experiments
Infinitely many-armed bandits
Multi-armed bandits
Continuous-space optimization

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