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<li class="toctree-l1"><a class="reference internal" href="README.html"><em>SMPyBandits</em></a></li>
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<li class="toctree-l1 current"><a class="current reference internal" href="#">List of research publications using Lilian Besson’s SMPyBandits project</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#st-article-about-policy-aggregation-algorithm-aka-model-selection">1st article, about <strong>policy aggregation algorithm (aka model selection)</strong></a></li>
<li class="toctree-l2"><a class="reference internal" href="#nd-article-about-multi-players-multi-armed-bandits">2nd article, about <strong>Multi-players Multi-Armed Bandits</strong></a></li>
<li class="toctree-l2"><a class="reference internal" href="#rd-article-using-doubling-trick-for-multi-armed-bandits">3rd article, using <strong>Doubling Trick for Multi-Armed Bandits</strong></a></li>
<li class="toctree-l2"><a class="reference internal" href="#th-article-about-piece-wise-stationary-multi-armed-bandits">4th article, about <strong>Piece-Wise Stationary Multi-Armed Bandits</strong></a></li>
<li class="toctree-l2"><a class="reference internal" href="#other-interesting-things">Other interesting things</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#single-player-policies">Single-player Policies</a></li>
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<div class="section" id="list-of-research-publications-using-lilian-besson-s-smpybandits-project">
<h1>List of research publications using Lilian Besson’s SMPyBandits project<a class="headerlink" href="#list-of-research-publications-using-lilian-besson-s-smpybandits-project" title="Permalink to this headline">¶</a></h1>
<p><a class="reference external" href="https://perso.crans.org/besson/">I (Lilian Besson)</a> have <a class="reference external" href="https://perso.crans.org/besson/phd/">started my PhD</a> in October 2016, and <a class="reference external" href="https://github.com/SMPyBandits/SMPyBandits/">this project</a> is a part of my <strong>on going</strong> research since December 2016.</p>
<hr class="docutils" />
<div class="section" id="st-article-about-policy-aggregation-algorithm-aka-model-selection">
<h2>1st article, about <a class="reference internal" href="Aggregation.html"><span class="doc">policy aggregation algorithm (aka model selection)</span></a><a class="headerlink" href="#st-article-about-policy-aggregation-algorithm-aka-model-selection" title="Permalink to this headline">¶</a></h2>
<p>I designed and added the <a class="reference external" href="https://smpybandits.github.io/docs/Policies.Aggregator.html"><code class="docutils literal notranslate"><span class="pre">Aggregator</span></code></a> policy, in order to test its validity and performance.</p>
<p>It is a “simple” <strong>voting algorithm to combine multiple bandit algorithms into one</strong>.
Basically, it behaves like a simple MAB bandit just based on empirical means (even simpler than UCB), where <em>arms</em> are the child algorithms <code class="docutils literal notranslate"><span class="pre">A_1</span> <span class="pre">..</span> <span class="pre">A_N</span></code>, each running in “parallel”.</p>
<blockquote>
<div><p><strong>For more details</strong>, refer to this file: <a class="reference internal" href="Aggregation.html"><span class="doc">Aggregation.md</span></a> and <a class="reference external" href="https://hal.inria.fr/hal-01705292">this research article</a>.</p>
</div></blockquote>
<blockquote>
<div><p>PDF : <a class="reference external" href="https://hal.inria.fr/hal-01705292/document">BKM_IEEEWCNC_2018.pdf</a> | HAL notice : <a class="reference external" href="https://hal.inria.fr/hal-01705292/">BKM_IEEEWCNC_2018</a> | BibTeX : <a class="reference external" href="https://hal.inria.fr/hal-01705292/bibtex">BKM_IEEEWCNC_2018.bib</a> | <a class="reference internal" href="Aggregation.html"><span class="doc">Source code and documentation</span></a>
<a class="reference external" href="https://hal.inria.fr/hal-01705292"><img alt="Published" src="https://img.shields.io/badge/Published%3F-accepted-green.svg" /></a> <a class="reference external" href="https://bitbucket.org/lbesson/aggregation-of-multi-armed-bandits-learning-algorithms-for/commits/"><img alt="Maintenance" src="https://img.shields.io/badge/Maintained%3F-finished-green.svg" /></a> <a class="reference external" href="https://bitbucket.org/lbesson/ama"><img alt="Ask Me Anything !" src="https://img.shields.io/badge/Ask%20me-anything-1abc9c.svg" /></a></p>
</div></blockquote>
</div>
<hr class="docutils" />
<div class="section" id="nd-article-about-multi-players-multi-armed-bandits">
<h2>2nd article, about <a class="reference internal" href="MultiPlayers.html"><span class="doc">Multi-players Multi-Armed Bandits</span></a><a class="headerlink" href="#nd-article-about-multi-players-multi-armed-bandits" title="Permalink to this headline">¶</a></h2>
<p>There is another point of view: instead of comparing different single-player policies on the same problem, we can make them play against each other, in a multi-player setting.
The basic difference is about <strong>collisions</strong> : at each time <code class="docutils literal notranslate"><span class="pre">t</span></code>, if two or more user chose to sense the same channel, there is a <em>collision</em>. Collisions can be handled in different way from the base station point of view, and from each player point of view.</p>
<blockquote>
<div><p><strong>For more details</strong>, refer to this file: <a class="reference internal" href="MultiPlayers.html"><span class="doc">MultiPlayers.md</span></a> and <a class="reference external" href="https://hal.inria.fr/hal-01629733">this research article</a>.</p>
</div></blockquote>
<blockquote>
<div><p>PDF : <a class="reference external" href="https://hal.inria.fr/hal-01629733/document">BK__ALT_2018.pdf</a> | HAL notice : <a class="reference external" href="https://hal.inria.fr/hal-01629733/">BK__ALT_2018</a> | BibTeX : <a class="reference external" href="https://hal.inria.fr/hal-01629733/bibtex">BK__ALT_2018.bib</a> | <a class="reference internal" href="MultiPlayers.html"><span class="doc">Source code and documentation</span></a>
<a class="reference external" href="http://www.cs.cornell.edu/conferences/alt2018/index.html#accepted"><img alt="Published" src="https://img.shields.io/badge/Published%3F-accepted-green.svg" /></a> <a class="reference external" href="https://bitbucket.org/lbesson/multi-player-bandits-revisited/commits/"><img alt="Maintenance" src="https://img.shields.io/badge/Maintained%3F-yes-green.svg" /></a> <a class="reference external" href="https://bitbucket.org/lbesson/ama"><img alt="Ask Me Anything !" src="https://img.shields.io/badge/Ask%20me-anything-1abc9c.svg" /></a></p>
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<hr class="docutils" />
<div class="section" id="rd-article-using-doubling-trick-for-multi-armed-bandits">
<h2>3rd article, using <a class="reference internal" href="DoublingTrick.html"><span class="doc">Doubling Trick for Multi-Armed Bandits</span></a><a class="headerlink" href="#rd-article-using-doubling-trick-for-multi-armed-bandits" title="Permalink to this headline">¶</a></h2>
<p>I studied what Doubling Trick can and can’t do to obtain efficient anytime version of non-anytime optimal Multi-Armed Bandits algorithms.</p>
<blockquote>
<div><p><strong>For more details</strong>, refer to this file: <a class="reference internal" href="DoublingTrick.html"><span class="doc">DoublingTrick.md</span></a> and <a class="reference external" href="https://hal.inria.fr/hal-01736357">this research article</a>.</p>
</div></blockquote>
<blockquote>
<div><p>PDF : <a class="reference external" href="https://hal.inria.fr/hal-01736357/document">BK__DoublingTricks_2018.pdf</a> | HAL notice : <a class="reference external" href="https://hal.inria.fr/hal-01736357/">BK__DoublingTricks_2018</a> | BibTeX : <a class="reference external" href="https://hal.inria.fr/hal-01736357/bibtex">BK__DoublingTricks_2018.bib</a> | <a class="reference internal" href="DoublingTrick.html"><span class="doc">Source code and documentation</span></a>
<a class="reference external" href="https://hal.inria.fr/hal-01736357"><img alt="Published" src="https://img.shields.io/badge/Published%3F-waiting-orange.svg" /></a> <a class="reference external" href="https://bitbucket.org/lbesson/what-doubling-tricks-can-and-cant-do-for-multi-armed-bandits/commits/"><img alt="Maintenance" src="https://img.shields.io/badge/Maintained%3F-almost%20finished-orange.svg" /></a> <a class="reference external" href="https://bitbucket.org/lbesson/ama"><img alt="Ask Me Anything !" src="https://img.shields.io/badge/Ask%20me-anything-1abc9c.svg" /></a></p>
</div></blockquote>
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<hr class="docutils" />
<div class="section" id="th-article-about-piece-wise-stationary-multi-armed-bandits">
<h2>4th article, about <a class="reference internal" href="NonStationaryBandits.html"><span class="doc">Piece-Wise Stationary Multi-Armed Bandits</span></a><a class="headerlink" href="#th-article-about-piece-wise-stationary-multi-armed-bandits" title="Permalink to this headline">¶</a></h2>
<p>With Emilie Kaufmann, we studied the Generalized Likelihood Ratio Test (GLRT) for sub-Bernoulli distributions, and proposed the B-GLRT algorithm for change-point detection for piece-wise stationary one-armed bandit problems. We combined the B-GLRT with the kl-UCB multi-armed bandit algorithm and proposed the GLR-klUCB algorithm for piece-wise stationary multi-armed bandit problems. We prove finite-time guarantees for the B-GLRT and the GLR-klUCB algorithm, and we illustrate its performance with extensive numerical experiments.</p>
<blockquote>
<div><p><strong>For more details</strong>, refer to this file: <a class="reference internal" href="NonStationaryBandits.html"><span class="doc">NonStationaryBandits.md</span></a> and <a class="reference external" href="https://hal.inria.fr/hal-02006471">this research article</a>.</p>
</div></blockquote>
<blockquote>
<div><p>PDF : <a class="reference external" href="https://hal.inria.fr/hal-02006471/document">BK__COLT_2019.pdf</a> | HAL notice : <a class="reference external" href="https://hal.inria.fr/hal-02006471/">BK__COLT_2019</a> | BibTeX : <a class="reference external" href="https://hal.inria.fr/hal-02006471/bibtex">BK__COLT_2019.bib</a> | <a class="reference external" href="NonStationaryBandits.html">Source code and documentation</a>
<a class="reference external" href="https://hal.inria.fr/hal-02006471"><img alt="Published" src="https://img.shields.io/badge/Published%3F-waiting-orange.svg" /></a> <a class="reference external" href="https://bitbucket.org/lbesson/combining-the-generalized-likelihood-ratio-test-and-kl-ucb-for/commits/"><img alt="Maintenance" src="https://img.shields.io/badge/Maintained%3F-almost%20finished-orange.svg" /></a> <a class="reference external" href="https://bitbucket.org/lbesson/ama"><img alt="Ask Me Anything !" src="https://img.shields.io/badge/Ask%20me-anything-1abc9c.svg" /></a></p>
</div></blockquote>
</div>
<hr class="docutils" />
<div class="section" id="other-interesting-things">
<h2>Other interesting things<a class="headerlink" href="#other-interesting-things" title="Permalink to this headline">¶</a></h2>
<div class="section" id="single-player-policies">
<h3><a class="reference external" href="https://smpybandits.github.io/docs/Policies.html">Single-player Policies</a><a class="headerlink" href="#single-player-policies" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><p>More than 65 algorithms, including all known variants of the <a class="reference external" href="https://smpybandits.github.io/docs/Policies.UCB.html"><code class="docutils literal notranslate"><span class="pre">UCB</span></code></a>, <a class="reference external" href="https://smpybandits.github.io/docs/Policies.klUCB.html">kl-UCB</a>, <a class="reference external" href="https://smpybandits.github.io/docs/Policies.MOSS.html"><code class="docutils literal notranslate"><span class="pre">MOSS</span></code></a> and <a class="reference external" href="https://smpybandits.github.io/docs/Policies.Thompson.html">Thompson Sampling</a> algorithms, as well as other less known algorithms (https://smpybandits.github.io/docs/<a class="reference external" href="Policies.OCUCB.html"><code class="docutils literal notranslate"><span class="pre">OCUCB</span></code></a>, <a class="reference external" href="https://smpybandits.github.io/docs/Policies.OCUCB.html"><code class="docutils literal notranslate"><span class="pre">BESA</span></code></a>, <a class="reference external" href="https://smpybandits.github.io/docs/Policies.OSSB.html"><code class="docutils literal notranslate"><span class="pre">OSSB</span></code></a> etc).</p></li>
<li><p><a class="reference external" href="https://smpybandits.github.io/docs/Policies.SparseWrapper.html#module-Policies.SparseWrapper"><code class="docutils literal notranslate"><span class="pre">SparseWrapper</span></code></a> is a generalization of <a class="reference external" href="https://arxiv.org/pdf/1706.01383/">the SparseUCB from this article</a>.</p></li>
<li><p>Implementation of very recent Multi-Armed Bandits algorithms, e.g., <a class="reference external" href="https://smpybandits.github.io/docs/Policies.klUCBPlusPlus.html"><code class="docutils literal notranslate"><span class="pre">kl-UCB++</span></code></a> (from <a class="reference external" href="https://hal.inria.fr/hal-01475078">this article</a>), <a class="reference external" href="https://smpybandits.github.io/docs/Policies.UCBdagger.html"><code class="docutils literal notranslate"><span class="pre">UCB-dagger</span></code></a> (from <a class="reference external" href="https://arxiv.org/pdf/1507.07880">this article</a>), or <a class="reference external" href="https://smpybandits.github.io/docs/Policies.MOSSAnytime.html"><code class="docutils literal notranslate"><span class="pre">MOSS-anytime</span></code></a> (from <a class="reference external" href="http://proceedings.mlr.press/v48/degenne16.pdf">this article</a>).</p></li>
<li><p>Experimental policies: <a class="reference external" href="https://smpybandits.github.io/docs/Policies.BlackBoxOpt.html"><code class="docutils literal notranslate"><span class="pre">BlackBoxOpt</span></code></a> or <a class="reference external" href="https://smpybandits.github.io/docs/Policies.UnsupervisedLearning.html"><code class="docutils literal notranslate"><span class="pre">UnsupervisedLearning</span></code></a> (using Gaussian processes to learn the arms distributions).</p></li>
</ul>
</div>
<div class="section" id="arms-and-problems">
<h3>Arms and problems<a class="headerlink" href="#arms-and-problems" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><p>My framework mainly targets stochastic bandits, with arms following <a class="reference external" href="https://smpybandits.github.io/docs/Arms.Bernoulli.html"><code class="docutils literal notranslate"><span class="pre">Bernoulli</span></code></a>, bounded (truncated) or unbounded <a class="reference external" href="https://smpybandits.github.io/docs/Arms.Gaussian.html"><code class="docutils literal notranslate"><span class="pre">Gaussian</span></code></a>, <a class="reference external" href="https://smpybandits.github.io/docs/Arms.Exponential.html"><code class="docutils literal notranslate"><span class="pre">Exponential</span></code></a>, <a class="reference external" href="https://smpybandits.github.io/docs/Arms.Gamma.html"><code class="docutils literal notranslate"><span class="pre">Gamma</span></code></a> or <a class="reference external" href="https://smpybandits.github.io/docs/Arms.Poisson.html"><code class="docutils literal notranslate"><span class="pre">Poisson</span></code></a> distributions.</p></li>
<li><p>The default configuration is to use a fixed problem for N repetitions (e.g. 1000 repetitions, use <a class="reference external" href="https://smpybandits.github.io/docs/Environment.MAB.html"><code class="docutils literal notranslate"><span class="pre">MAB.MAB</span></code></a>), but there is also a perfect support for “Bayesian” problems where the mean vector µ1,…,µK change <em>at every repetition</em> (see <a class="reference external" href="https://smpybandits.github.io/docs/Environment.MAB.html"><code class="docutils literal notranslate"><span class="pre">MAB.DynamicMAB</span></code></a>).</p></li>
<li><p>There is also a good support for Markovian problems, see <a class="reference external" href="https://smpybandits.github.io/docs/Environment.MAB.html#Environment.MAB.MarkovianMAB"><code class="docutils literal notranslate"><span class="pre">MAB.MarkovianMAB</span></code></a>, even though I didn’t implement any policies tailored for Markovian problems.</p></li>
<li><p>I’m actively working on adding a very clean support for non-stationary MAB problems, and <a class="reference external" href="https://smpybandits.github.io/docs/Environment.MAB.html#Environment.MAB.PieceWiseStationaryMAB"><code class="docutils literal notranslate"><span class="pre">MAB.PieceWiseStationaryMAB</span></code></a> is already working well. Use it with policies designed for piece-wise stationary problems, like <a class="reference external" href="https://smpybandits.github.io/docs/Policies.DiscountedThompson.html">Discounted-Thompson</a>, <a class="reference external" href="https://smpybandits.github.io/docs/Policies.CD_UCB.html">CD-UCB</a>, <a class="reference external" href="https://smpybandits.github.io/docs/Policies.Monitored_UCB.html">M-UCB</a>, <a class="reference external" href="https://smpybandits.github.io/docs/Policies.SWHash_UCB.html">SW-UCB#</a>.</p></li>
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<h3>📜 License ? <a class="reference external" href="https://github.com/SMPyBandits/SMPyBandits/blob/master/LICENSE"><img alt="GitHub license" src="https://img.shields.io/github/license/SMPyBandits/SMPyBandits.svg" /></a><a class="headerlink" href="#scroll-license-github-license" title="Permalink to this headline">¶</a></h3>
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<p>© 2016-2018 <a class="reference external" href="https://GitHub.com/Naereen">Lilian Besson</a>.</p>
<p>Note: I have worked on other topics during <a class="reference external" href="https://perso.crans.org/besson/phd/">my PhD</a>, you can find my research articles <a class="reference external" href="https://perso.crans.org/besson/articles/">on my website</a>, or have a look to <a class="reference external" href="https://scholar.google.com/citations?hl=en&user=bt3upq8AAAAJ">my Google Scholar profile</a> or <a class="reference external" href="https://cv.archives-ouvertes.fr/lilian-besson">résumé on HAL</a>.</p>
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