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dc.contributor.author
Sessa, Pier Giuseppe
dc.contributor.author
Bogunovic, Ilija
dc.contributor.author
Krause, Andreas
dc.contributor.author
Kamgarpour, Maryam
dc.contributor.editor
Larochelle, Hugo
dc.contributor.editor
Ranzato, Marc'Aurelio
dc.contributor.editor
Hadsell, Raia
dc.contributor.editor
Balcan, Maria F.
dc.contributor.editor
Lin, H.
dc.date.accessioned
2021-07-21T07:18:12Z
dc.date.available
2020-12-17T16:18:16Z
dc.date.available
2020-12-22T08:21:24Z
dc.date.available
2021-01-05T07:21:12Z
dc.date.available
2021-03-02T14:47:39Z
dc.date.available
2021-07-21T07:18:12Z
dc.date.issued
2021
dc.identifier.isbn
978-1-7138-2954-6
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/456947
dc.description.abstract
We formulate the novel class of contextual games, a type of repeated games driven by contextual information at each round. By means of kernel-based regularity assumptions, we model the correlation between different contexts and game outcomes and propose a novel online (meta) algorithm that exploits such correlations to minimize the contextual regret of individual players. We define game-theoretic notions of contextual Coarse Correlated Equilibria (c-CCE) and optimal contextual welfare for this new class of games and show that c-CCEs and optimal welfare can be approached whenever players' contextual regrets vanish. Finally, we empirically validate our results in a traffic routing experiment, where our algorithm leads to better performance and higher welfare compared to baselines that do not exploit the available contextual information or the correlations present in the game.
en_US
dc.language.iso
en
en_US
dc.publisher
Curran
en_US
dc.title
Contextual Games: Multi-Agent Learning with Side Information
en_US
dc.type
Conference Paper
dc.date.published
2020
ethz.book.title
Advances in Neural Information Processing Systems 33
en_US
ethz.pages.start
21912
en_US
ethz.pages.end
21922
en_US
ethz.event
34th Annual Conference on Neural Information Processing Systems (NeurIPS 2020)
en_US
ethz.event.location
Online
en_US
ethz.event.date
December 6-12, 2020
en_US
ethz.notes
Due to the Coronavirus (COVID-19) the conference was conducted virtually.
en_US
ethz.grant
Reliable Data-Driven Decision Making in Cyber-Physical Systems
en_US
ethz.grant
Robust Sample-Efficient Learning when Data ist Costly
en_US
ethz.publication.place
Red Hook, NY
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02661 - Institut für Maschinelles Lernen / Institute for Machine Learning::03908 - Krause, Andreas / Krause, Andreas
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02650 - Institut für Automatik / Automatic Control Laboratory::09578 - Kamgarpour, Maryam (ehemalig) / Kamgarpour, Maryam (former)
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02661 - Institut für Maschinelles Lernen / Institute for Machine Learning::03908 - Krause, Andreas / Krause, Andreas
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02650 - Institut für Automatik / Automatic Control Laboratory::09578 - Kamgarpour, Maryam (ehemalig) / Kamgarpour, Maryam (former)
en_US
ethz.identifier.url
https://papers.nips.cc/paper/2020/hash/f9afa97535cf7c8789a1c50a2cd83787-Abstract.html
ethz.grant.agreementno
815943
ethz.grant.agreementno
815943
ethz.grant.agreementno
19-2 FEL-47
ethz.grant.agreementno
815943
ethz.grant.agreementno
19-2 FEL-47
ethz.grant.fundername
EC
ethz.grant.fundername
EC
ethz.grant.fundername
ETHZ
ethz.grant.fundername
EC
ethz.grant.fundername
ETHZ
ethz.grant.funderDoi
10.13039/501100000780
ethz.grant.funderDoi
10.13039/501100000780
ethz.grant.funderDoi
10.13039/501100003006
ethz.grant.funderDoi
10.13039/501100000780
ethz.grant.funderDoi
10.13039/501100003006
ethz.grant.program
H2020
ethz.grant.program
H2020
ethz.grant.program
ETH Fellows
ethz.grant.program
H2020
ethz.grant.program
ETH Fellows
ethz.date.deposited
2020-12-17T16:18:24Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-03-02T14:47:48Z
ethz.rosetta.lastUpdated
2022-03-29T10:33:23Z
ethz.rosetta.versionExported
true
ethz.COinS
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