<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>8397</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Efficient Online-Bandit Strategies for Minimax Learning Problems</title>
    <abstract language="eng">Several learning problems involve solving min-max problems, e.g., empirical distributional robust learning&#13;
[Namkoong and Duchi, 2016, Curi et al., 2020] or learning with non-standard aggregated losses [Shalev-&#13;
Shwartz and Wexler, 2016, Fan et al., 2017]. More specifically, these problems are convex-linear problems&#13;
where the minimization is carried out over the model parameters w ∈ W and the maximization over the&#13;
empirical distribution p ∈ K of the training set indexes, where K is the simplex or a subset of it. To design&#13;
efficient methods, we let an online learning algorithm play against a (combinatorial) bandit algorithm.&#13;
We argue that the efficiency of such approaches critically depends on the structure of K and propose two&#13;
properties of K that facilitate designing efficient algorithms. We focus on a specific family of sets Sn,k&#13;
encompassing various learning applications and provide high-probability convergence guarantees to the&#13;
minimax values.</abstract>
    <enrichment key="PeerReviewed">no</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Christophe Roux</author>
    <submitter> Roux</submitter>
    <author>Sebastian Pokutta</author>
    <author>Elias Wirth</author>
    <author>Thomas Kerdreux</author>
    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="persons" number="pokutta">Pokutta, Sebastian</collection>
    <collection role="institutes" number="ais2t">AI in Society, Science, and Technology</collection>
    <collection role="persons" number="Roux">Roux, Christophe</collection>
    <collection role="projects" number="Math+AA3-7">Math+AA3-7</collection>
  </doc>
</export-example>
