Publikation:

Generalized Maximum Entropy Estimation

Lade...
Vorschaubild

Dateien

Sutter_2-1svqydo8rxhan3.pdf
Sutter_2-1svqydo8rxhan3.pdfGröße: 562.66 KBDownloads: 107

Datum

2019

Autor:innen

Sutter, David
Esfahani, Peyman Mohajerin
Lygeros, John

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

DOI (zitierfähiger Link)
ArXiv-ID

Internationale Patentnummer

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung
Open Access Hybrid
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published

Erschienen in

Journal of Machine Learning Research. Microtome Publishing. 2019, 20, 138. ISSN 1532-4435. eISSN 1533-7928

Zusammenfassung

We consider the problem of estimating a probability distribution that maximizes the entropy while satisfying a finite number of moment constraints, possibly corrupted by noise. Based on duality of convex programming, we present a novel approximation scheme using a smoothed fast gradient method that is equipped with explicit bounds on the approximation error. We further demonstrate how the presented scheme can be used for approximating the chemical master equation through the zero-information moment closure method, and for an approximate dynamic programming approach in the context of constrained Markov decision processes with uncountable state and action spaces.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
004 Informatik

Schlagwörter

Entropy maximization, convex optimization, relative entropy minimization, fast gradient method, approximate dynamic programming

Konferenz

Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690SUTTER, Tobias, David SUTTER, Peyman Mohajerin ESFAHANI, John LYGEROS, 2019. Generalized Maximum Entropy Estimation. In: Journal of Machine Learning Research. Microtome Publishing. 2019, 20, 138. ISSN 1532-4435. eISSN 1533-7928
BibTex
@article{Sutter2019Gener-55731,
  year={2019},
  title={Generalized Maximum Entropy Estimation},
  url={https://jmlr.org/papers/v20/17-486.html},
  volume={20},
  issn={1532-4435},
  journal={Journal of Machine Learning Research},
  author={Sutter, Tobias and Sutter, David and Esfahani, Peyman Mohajerin and Lygeros, John},
  note={Article Number: 138}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/55731">
    <dc:creator>Sutter, David</dc:creator>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-12-02T11:58:34Z</dc:date>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/55731/1/Sutter_2-1svqydo8rxhan3.pdf"/>
    <dc:contributor>Sutter, David</dc:contributor>
    <dc:creator>Lygeros, John</dc:creator>
    <dc:rights>terms-of-use</dc:rights>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:creator>Esfahani, Peyman Mohajerin</dc:creator>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/55731"/>
    <dcterms:issued>2019</dcterms:issued>
    <dcterms:abstract xml:lang="eng">We consider the problem of estimating a probability distribution that maximizes the entropy while satisfying a finite number of moment constraints, possibly corrupted by noise. Based on duality of convex programming, we present a novel approximation scheme using a smoothed fast gradient method that is equipped with explicit bounds on the approximation error. We further demonstrate how the presented scheme can be used for approximating the chemical master equation through the zero-information moment closure method, and for an approximate dynamic programming approach in the context of constrained Markov decision processes with uncountable state and action spaces.</dcterms:abstract>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-12-02T11:58:34Z</dcterms:available>
    <dc:creator>Sutter, Tobias</dc:creator>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/55731/1/Sutter_2-1svqydo8rxhan3.pdf"/>
    <dc:contributor>Sutter, Tobias</dc:contributor>
    <dcterms:title>Generalized Maximum Entropy Estimation</dcterms:title>
    <dc:language>eng</dc:language>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:contributor>Esfahani, Peyman Mohajerin</dc:contributor>
    <dc:contributor>Lygeros, John</dc:contributor>
  </rdf:Description>
</rdf:RDF>

Interner Vermerk

xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter

Kontakt
URL der Originalveröffentl.

Prüfdatum der URL

2021-12-02

Prüfungsdatum der Dissertation

Finanzierungsart

Kommentar zur Publikation

Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Nein
Begutachtet
Ja
Diese Publikation teilen