<?xml version="1.0" encoding="utf-8"?>
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    <id>9414</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>5950</pageFirst>
    <pageLast>5956</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>195</volume>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
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    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Open problem: polynomial linearly-convergent method for g-convex optimization?</title>
    <parentTitle language="eng">Proceedings of Thirty Sixth Conference on Learning Theory, PMLR</parentTitle>
    <identifier type="url">https://proceedings.mlr.press/v195/criscitiello23b.html</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Christopher Criscitiello</author>
    <submitter>Christoph Spiegel</submitter>
    <author>David Martínez-Rubio</author>
    <author>Nicolas Boumal</author>
    <collection role="institutes" number="ais2t">AI in Society, Science, and Technology</collection>
  </doc>
</export-example>
