<?xml version="1.0" encoding="utf-8"?>
<TEI xmlns="http://www.tei-c.org/ns/1.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:hal="http://hal.archives-ouvertes.fr/" xmlns:gml="http://www.opengis.net/gml/3.3/" xmlns:gmlce="http://www.opengis.net/gml/3.3/ce" version="1.1" xsi:schemaLocation="http://www.tei-c.org/ns/1.0 http://api.archives-ouvertes.fr/documents/aofr-sword.xsd">
  <teiHeader>
    <fileDesc>
      <titleStmt>
        <title>HAL TEI export of hal-03811810</title>
      </titleStmt>
      <publicationStmt>
        <distributor>CCSD</distributor>
        <availability status="restricted">
          <licence target="https://creativecommons.org/publicdomain/zero/1.0/">CC0 1.0 - Universal</licence>
        </availability>
        <date when="2026-05-27T17:35:45+02:00"/>
      </publicationStmt>
      <sourceDesc>
        <p part="N">HAL API Platform</p>
      </sourceDesc>
    </fileDesc>
  </teiHeader>
  <text>
    <body>
      <listBibl>
        <biblFull>
          <titleStmt>
            <title xml:lang="en">FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels</title>
            <author role="aut">
              <persName>
                <forename type="first">Guillaume</forename>
                <surname>Staerman</surname>
              </persName>
              <email type="md5">ba00e776b3beff8866402cbd0b3329dd</email>
              <email type="domain">telecom-paris.fr</email>
              <idno type="idhal" notation="string">guillaume-staerman</idno>
              <idno type="idhal" notation="numeric">743604</idno>
              <idno type="halauthorid" notation="string">47196-743604</idno>
              <idno type="GOOGLE SCHOLAR">https://scholar.google.com/citations?user=Zb2ax0wAAAAJ&amp;hl=fr</idno>
              <affiliation ref="#struct-1124248"/>
            </author>
            <author role="aut">
              <persName>
                <forename type="first">Cédric</forename>
                <surname>Allain</surname>
              </persName>
              <idno type="halauthorid">2320173-0</idno>
              <affiliation ref="#struct-1124248"/>
            </author>
            <author role="aut">
              <persName>
                <forename type="first">Alexandre</forename>
                <surname>Gramfort</surname>
              </persName>
              <email type="md5">9976bec20103e6a7d904579269b51915</email>
              <email type="domain">inria.fr</email>
              <idno type="idhal" notation="string">agramfort</idno>
              <idno type="idhal" notation="numeric">687</idno>
              <idno type="halauthorid" notation="string">16903-687</idno>
              <idno type="ORCID">https://orcid.org/0000-0001-9791-4404</idno>
              <idno type="IDREF">https://www.idref.fr/169233758</idno>
              <affiliation ref="#struct-1124248"/>
            </author>
            <author role="aut">
              <persName>
                <forename type="first">Thomas</forename>
                <surname>Moreau</surname>
              </persName>
              <email type="md5">d772a9440dbec6619b3609c4598dc84c</email>
              <email type="domain">inria.fr</email>
              <idno type="idhal" notation="string">tommoral</idno>
              <idno type="idhal" notation="numeric">171108</idno>
              <idno type="halauthorid" notation="string">42905-171108</idno>
              <idno type="ORCID">https://orcid.org/0000-0002-1523-3419</idno>
              <idno type="GOOGLE SCHOLAR">https://scholar.google.fr/citations?user=HEO_PsAAAAAJ&amp;hl=fr</idno>
              <affiliation ref="#struct-1124248"/>
            </author>
            <editor role="depositor">
              <persName>
                <forename>Guillaume</forename>
                <surname>Staerman</surname>
              </persName>
              <email type="md5">140bafdeb1ab4f048464d7624140a9dc</email>
              <email type="domain">gmail.com</email>
            </editor>
          </titleStmt>
          <editionStmt>
            <edition n="v1" type="current">
              <date type="whenSubmitted">2022-10-12 10:51:21</date>
              <date type="whenModified">2025-10-24 16:46:02</date>
              <date type="whenReleased">2022-10-12 10:51:21</date>
              <date type="whenProduced">2023-07-25</date>
              <ref type="externalLink" target="http://arxiv.org/pdf/2210.04635"/>
            </edition>
            <respStmt>
              <resp>contributor</resp>
              <name key="987057">
                <persName>
                  <forename>Guillaume</forename>
                  <surname>Staerman</surname>
                </persName>
                <email type="md5">140bafdeb1ab4f048464d7624140a9dc</email>
                <email type="domain">gmail.com</email>
              </name>
            </respStmt>
          </editionStmt>
          <publicationStmt>
            <distributor>CCSD</distributor>
            <idno type="halId">hal-03811810</idno>
            <idno type="halUri">https://hal.science/hal-03811810</idno>
            <idno type="halBibtex">staerman:hal-03811810</idno>
            <idno type="halRefHtml">&lt;i&gt;International Conference on Machine Learning&lt;/i&gt;, Jul 2023, Honololu, Hawaii, United States. pp.32575-32597</idno>
            <idno type="halRef">International Conference on Machine Learning, Jul 2023, Honololu, Hawaii, United States. pp.32575-32597</idno>
            <availability status="restricted"/>
          </publicationStmt>
          <seriesStmt>
            <idno type="stamp" n="CEA">CEA - Commissariat à l'énergie atomique</idno>
            <idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno>
            <idno type="stamp" n="INRIA-SACLAY" corresp="INRIA">INRIA Saclay - Ile de France</idno>
            <idno type="stamp" n="INRIA_TEST">INRIA - Institut National de Recherche en Informatique et en Automatique</idno>
            <idno type="stamp" n="TESTALAIN1">TESTALAIN1</idno>
            <idno type="stamp" n="INRIA2">INRIA 2</idno>
            <idno type="stamp" n="UNIV-PARIS-SACLAY">Université Paris-Saclay</idno>
            <idno type="stamp" n="UNIVERSITE-PARIS-SACLAY" corresp="UNIV-PARIS-SACLAY">Université Paris-Saclay</idno>
            <idno type="stamp" n="GS-COMPUTER-SCIENCE">Graduate School Computer Science</idno>
          </seriesStmt>
          <notesStmt>
            <note type="audience" n="2">International</note>
            <note type="invited" n="0">No</note>
            <note type="popular" n="0">No</note>
            <note type="peer" n="1">Yes</note>
            <note type="proceedings" n="1">Yes</note>
          </notesStmt>
          <sourceDesc>
            <biblStruct>
              <analytic>
                <title xml:lang="en">FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels</title>
                <author role="aut">
                  <persName>
                    <forename type="first">Guillaume</forename>
                    <surname>Staerman</surname>
                  </persName>
                  <email type="md5">ba00e776b3beff8866402cbd0b3329dd</email>
                  <email type="domain">telecom-paris.fr</email>
                  <idno type="idhal" notation="string">guillaume-staerman</idno>
                  <idno type="idhal" notation="numeric">743604</idno>
                  <idno type="halauthorid" notation="string">47196-743604</idno>
                  <idno type="GOOGLE SCHOLAR">https://scholar.google.com/citations?user=Zb2ax0wAAAAJ&amp;hl=fr</idno>
                  <affiliation ref="#struct-1124248"/>
                </author>
                <author role="aut">
                  <persName>
                    <forename type="first">Cédric</forename>
                    <surname>Allain</surname>
                  </persName>
                  <idno type="halauthorid">2320173-0</idno>
                  <affiliation ref="#struct-1124248"/>
                </author>
                <author role="aut">
                  <persName>
                    <forename type="first">Alexandre</forename>
                    <surname>Gramfort</surname>
                  </persName>
                  <email type="md5">9976bec20103e6a7d904579269b51915</email>
                  <email type="domain">inria.fr</email>
                  <idno type="idhal" notation="string">agramfort</idno>
                  <idno type="idhal" notation="numeric">687</idno>
                  <idno type="halauthorid" notation="string">16903-687</idno>
                  <idno type="ORCID">https://orcid.org/0000-0001-9791-4404</idno>
                  <idno type="IDREF">https://www.idref.fr/169233758</idno>
                  <affiliation ref="#struct-1124248"/>
                </author>
                <author role="aut">
                  <persName>
                    <forename type="first">Thomas</forename>
                    <surname>Moreau</surname>
                  </persName>
                  <email type="md5">d772a9440dbec6619b3609c4598dc84c</email>
                  <email type="domain">inria.fr</email>
                  <idno type="idhal" notation="string">tommoral</idno>
                  <idno type="idhal" notation="numeric">171108</idno>
                  <idno type="halauthorid" notation="string">42905-171108</idno>
                  <idno type="ORCID">https://orcid.org/0000-0002-1523-3419</idno>
                  <idno type="GOOGLE SCHOLAR">https://scholar.google.fr/citations?user=HEO_PsAAAAAJ&amp;hl=fr</idno>
                  <affiliation ref="#struct-1124248"/>
                </author>
              </analytic>
              <monogr>
                <title level="m">PMLR</title>
                <meeting>
                  <title>International Conference on Machine Learning</title>
                  <date type="start">2023-07-25</date>
                  <date type="end">2023-07-27</date>
                  <settlement>Honololu, Hawaii</settlement>
                  <country key="US">United States</country>
                </meeting>
                <imprint>
                  <biblScope unit="volume">202</biblScope>
                  <biblScope unit="pp">32575-32597</biblScope>
                </imprint>
              </monogr>
              <idno type="arxiv">2210.04635</idno>
            </biblStruct>
          </sourceDesc>
          <profileDesc>
            <langUsage>
              <language ident="en">English</language>
            </langUsage>
            <textClass>
              <keywords scheme="author">
                <term xml:lang="en">Point processes</term>
              </keywords>
              <classCode scheme="halDomain" n="info.info-lg">Computer Science [cs]/Machine Learning [cs.LG]</classCode>
              <classCode scheme="halDomain" n="scco.neur">Cognitive science/Neuroscience</classCode>
              <classCode scheme="halTypology" n="COMM">Conference papers</classCode>
              <classCode scheme="halOldTypology" n="COMM">Conference papers</classCode>
              <classCode scheme="halTreeTypology" n="COMM">Conference papers</classCode>
            </textClass>
            <abstract xml:lang="en">
              <p>Temporal point processes (TPP) are a natural tool for modeling event-based data. Among all TPP models, Hawkes processes have proven to be the most widely used, mainly due to their simplicity and computational ease when considering exponential or non-parametric kernels. Although non-parametric kernels are an option, such models require large datasets. While exponential kernels are more data efficient and relevant for certain applications where events immediately trigger more events, they are ill-suited for applications where latencies need to be estimated, such as in neuroscience. This work aims to offer an efficient solution to TPP inference using general parametric kernels with finite support. The developed solution consists of a fast L2 gradient-based solver leveraging a discretized version of the events. After supporting the use of discretization theoretically, the statistical and computational efficiency of the novel approach is demonstrated through various numerical experiments. Finally, the effectiveness of the method is evaluated by modeling the occurrence of stimuli-induced patterns from brain signals recorded with magnetoencephalography (MEG). Given the use of general parametric kernels, results show that the proposed approach leads to a more plausible estimation of pattern latency compared to the state-of-the-art.</p>
            </abstract>
          </profileDesc>
        </biblFull>
      </listBibl>
    </body>
    <back>
      <listOrg type="structures">
        <org type="researchteam" xml:id="struct-1124248" status="VALID">
          <idno type="RNSR">202224253W</idno>
          <idno type="ROR">https://ror.org/00dfknt61</idno>
          <orgName>Modèles et inférence pour les données de Neuroimagerie</orgName>
          <orgName type="acronym">MIND</orgName>
          <date type="start">2022-04-01</date>
          <date type="end">2026-03-31</date>
          <desc>
            <address>
              <addrLine>1 rue Honoré d'Estienne d'Orves Campus de l'École Polytechnique 91120 Palaiseau</addrLine>
              <country key="FR"/>
            </address>
          </desc>
          <listRelation>
            <relation active="#struct-118170" type="direct"/>
            <relation active="#struct-300016" type="indirect"/>
            <relation active="#struct-1225627" type="direct"/>
            <relation active="#struct-118511" type="indirect"/>
            <relation active="#struct-300009" type="indirect"/>
          </listRelation>
        </org>
        <org type="laboratory" xml:id="struct-118170" status="VALID">
          <orgName>IFR49 - Neurospin - CEA</orgName>
          <desc>
            <address>
              <addrLine>Gif sur Yvette</addrLine>
              <country key="FR"/>
            </address>
          </desc>
          <listRelation>
            <relation active="#struct-300016" type="direct"/>
          </listRelation>
        </org>
        <org type="institution" xml:id="struct-300016" status="VALID">
          <idno type="IdRef">026372061</idno>
          <idno type="ISNI">0000000122998025</idno>
          <idno type="ROR">https://ror.org/00jjx8s55</idno>
          <idno type="Wikidata">Q868550</idno>
          <orgName>Commissariat à l'énergie atomique et aux énergies alternatives</orgName>
          <orgName type="acronym">CEA</orgName>
          <desc>
            <address>
              <addrLine>Centre de SaclayCentre de GrenobleCentre de Cadaracheetc</addrLine>
              <country key="FR"/>
            </address>
            <ref type="url">http://www.cea.fr/</ref>
          </desc>
        </org>
        <org type="department" xml:id="struct-1225627" status="VALID">
          <idno type="ROR">https://ror.org/040753f36</idno>
          <orgName>Centre Inria de l'Université Paris-Saclay</orgName>
          <date type="start">2022-11-01</date>
          <desc>
            <address>
              <addrLine>9 Rue Joliot Curie, 91190 Gif-sur-Yvette</addrLine>
              <country key="FR"/>
            </address>
          </desc>
          <listRelation>
            <relation active="#struct-118511" type="direct"/>
            <relation active="#struct-300009" type="indirect"/>
          </listRelation>
        </org>
        <org type="laboratory" xml:id="struct-118511" status="VALID">
          <idno type="RNSR">200818248E</idno>
          <idno type="ROR">https://ror.org/0315e5x55</idno>
          <orgName>Centre Inria de Saclay</orgName>
          <desc>
            <address>
              <addrLine>1 rue Honoré d'Estienne d'OrvesBâtiment Alan TuringCampus de l'École Polytechnique91120 Palaiseau</addrLine>
              <country key="FR"/>
            </address>
            <ref type="url">http://www.inria.fr/centre/saclay</ref>
          </desc>
          <listRelation>
            <relation active="#struct-300009" type="direct"/>
          </listRelation>
        </org>
        <org type="institution" xml:id="struct-300009" status="VALID">
          <idno type="ROR">https://ror.org/02kvxyf05</idno>
          <orgName>Institut National de Recherche en Informatique et en Automatique</orgName>
          <orgName type="acronym">Inria</orgName>
          <desc>
            <address>
              <addrLine>Domaine de VoluceauRocquencourt - BP 10578153 Le Chesnay Cedex</addrLine>
              <country key="FR"/>
            </address>
            <ref type="url">http://www.inria.fr/en/</ref>
          </desc>
        </org>
      </listOrg>
    </back>
  </text>
</TEI>