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dc.contributor.author
Mutný, Mojmír
dc.contributor.author
Krause, Andreas
dc.contributor.editor
Camps-Valls, Gustau
dc.contributor.editor
Ruiz, Francisco J.R.
dc.contributor.editor
Valera, Isabel
dc.date.accessioned
2022-10-28T07:29:05Z
dc.date.available
2022-10-28T03:15:29Z
dc.date.available
2022-10-28T07:29:05Z
dc.date.issued
2022
dc.identifier.issn
2640-3498
dc.identifier.uri
http://hdl.handle.net/20.500.11850/578162
dc.description.abstract
We study adaptive sensing of Cox point processes, a widely used model from spatial statistics. We introduce three tasks: maximization of captured events, search for the maximum of the intensity function and learning level sets of the intensity function. We model the intensity function as a sample from a truncated Gaussian process, represented in a specially constructed positive basis. In this basis, the positivity constraint on the intensity function has a simple form. We show how the minimal description positive basis can be adapted to the covariance kernel, to non-stationarity and make connections to common positive bases from prior works. Our adaptive sensing algorithms use Langevin dynamics and are based on posterior sampling (Cox-TnomPsoN) and top-two posterior sampling (ToP2) principles. With latter, the difference between samples serves as a surrogate to the uncertainty. We demonstrate the approach using examples from environmental monitoring and crime rate modeling, and compare it to the classical Bayesian experimental design approach.
en_US
dc.language.iso
en
en_US
dc.publisher
PMLR
en_US
dc.title
Sensing Cox Processes via Posterior Sampling and Positive Bases
en_US
dc.type
Conference Paper
ethz.book.title
Proceedings of The 25th International Conference on Artificial Intelligence and Statistics
en_US
ethz.journal.title
Proceedings of Machine Learning Research
ethz.journal.volume
151
en_US
ethz.pages.start
6968
en_US
ethz.pages.end
6989
en_US
ethz.event
25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022)
en_US
ethz.event.location
Online
en_US
ethz.event.date
March 28-30, 2022
en_US
ethz.grant
NCCR Catalysis (phase I)
en_US
ethz.identifier.wos
ethz.publication.place
Cambridge, MA
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
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
ethz.identifier.url
https://proceedings.mlr.press/v151/mutny22a.html
ethz.grant.agreementno
180544
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
NCCR full proposal
ethz.date.deposited
2022-10-28T03:15:40Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2022-10-28T07:29:06Z
ethz.rosetta.lastUpdated
2024-02-02T18:49:38Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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