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@ -194,7 +194,7 @@ window.Quarto = {
<li><a href="#journaux" id="toc-journaux" class="nav-link active" data-scroll-target="#journaux">Journaux</a></li>
</ul>
</nav>
<h5 class="quarto-listing-category-title">Catégories</h5><div class="quarto-listing-category category-default"><div class="category" data-category="">Tous <span class="quarto-category-count">(16)</span></div><div class="category" data-category="Y29sQmlTQk0=">colBiSBM <span class="quarto-category-count">(16)</span></div><div class="category" data-category="R05O">GNN <span class="quarto-category-count">(9)</span></div><div class="category" data-category="aW5mJUMzJUE5cmVuY2U=">inférence <span class="quarto-category-count">(11)</span></div></div></div>
<h5 class="quarto-listing-category-title">Catégories</h5><div class="quarto-listing-category category-default"><div class="category" data-category="">Tous <span class="quarto-category-count">(17)</span></div><div class="category" data-category="Y29sQmlTQk0=">colBiSBM <span class="quarto-category-count">(17)</span></div><div class="category" data-category="R05O">GNN <span class="quarto-category-count">(10)</span></div><div class="category" data-category="aW5mJUMzJUE5cmVuY2U=">inférence <span class="quarto-category-count">(12)</span></div></div></div>
<!-- main -->
<main class="content" id="quarto-document-content">
@ -224,14 +224,14 @@ window.Quarto = {
<div>
<div class="quarto-title-meta-heading">Date de publication</div>
<div class="quarto-title-meta-contents">
<p class="date">17 août 2025</p>
<p class="date">26 août 2025</p>
</div>
</div>
<div>
<div class="quarto-title-meta-heading">Modifié</div>
<div class="quarto-title-meta-contents">
<p class="date-modified">17 août 2025</p>
<p class="date-modified">26 août 2025</p>
</div>
</div>
@ -263,7 +263,41 @@ Agenda
<h2 class="anchored" data-anchor-id="journaux">Journaux</h2>
<div id="listing-journal-these" class="quarto-listing quarto-listing-container-default">
<div class="list quarto-listing-default">
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<div class="thumbnail"><a href="./suivi/2025-35/2025-35.html" class="no-external">
<div class="listing-item-img-placeholder card-img-top" >&nbsp;</div>
</a></div>
<div class="body">
<h3 class="no-anchor listing-title">
<a href="./suivi/2025-35/2025-35.html" class="no-external">Bilan semaine 35 2025 : 25 août - 29 août</a>
</h3>
<div class="listing-categories">
<div class="listing-category" onclick="window.quartoListingCategory('Y29sQmlTQk0='); return false;">colBiSBM</div>
<div class="listing-category" onclick="window.quartoListingCategory('aW5mJUMzJUE5cmVuY2U='); return false;">inférence</div>
<div class="listing-category" onclick="window.quartoListingCategory('R05O'); return false;">GNN</div>
</div>
<div class="delink listing-description"><a href="./suivi/2025-35/2025-35.html" class="no-external">
En Bernoulli pas de forme analytique non plus : Pour <span class="math inline">\alpha_{qr}</span>: <span class="math display"> \sum_{m=1}^M \sum_{i=1}^{n_1^m} \sum_{j=1}^{n_2^m}…</span><span class="math display"></span>
</a></div>
</div>
<div class="metadata">
<a href="./suivi/2025-35/2025-35.html" class="no-external">
<div class="listing-date">
29 août 2025
</div>
<div class="listing-author">
Louis Lacoste
</div>
</a>
</div>
</div>
<div class="quarto-post image-right" data-index="1" data-categories="Y29sQmlTQk0lMkNpbmYlQzMlQTlyZW5jZSUyQ0dOTg==" data-listing-date-sort="1755129600000" data-listing-file-modified-sort="1756198639588" data-listing-date-modified-sort="1756198639000" data-listing-reading-time-sort="4" data-listing-word-count-sort="792">
<div class="thumbnail"><a href="./suivi/2025-33/2025-33.html" class="no-external">
<p class="card-img-top"><img src="suivi/2025-33/figs/auc-model.png" class="thumbnail-image card-img"/></p>
@ -298,7 +332,7 @@ Louis Lacoste
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@ -333,7 +367,7 @@ Louis Lacoste
</a>
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</div>
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@ -368,7 +402,7 @@ Louis Lacoste
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<div class="thumbnail"><a href="./suivi/2025-27/2025-27.html" class="no-external">
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@ -403,7 +437,7 @@ Louis Lacoste
</a>
</div>
</div>
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<div class="thumbnail"><a href="./suivi/2025-25/2025-25.html" class="no-external">
<div class="listing-item-img-placeholder card-img-top" >&nbsp;</div>
@ -438,7 +472,7 @@ Louis Lacoste
</a>
</div>
</div>
<div class="quarto-post image-right" data-index="5" data-categories="Y29sQmlTQk0lMkNpbmYlQzMlQTlyZW5jZSUyQ0dOTg==" data-listing-date-sort="1749772800000" data-listing-file-modified-sort="1755444925996" data-listing-date-modified-sort="1755444925000" data-listing-reading-time-sort="6" data-listing-word-count-sort="1009">
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<div class="thumbnail"><a href="./suivi/2025-24/2025-24.html" class="no-external">
<p class="card-img-top"><img src="suivi/2025-24/figs/ari-clustering-desc&asc9.png" alt="9 réseaux - ARI pour le clustering avec modèles iid, procédure descendante et descendante&ascendante" class="thumbnail-image card-img"/></p>
@ -472,7 +506,7 @@ Louis Lacoste
</a>
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@ -507,7 +541,7 @@ Louis Lacoste
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<div class="listing-item-img-placeholder card-img-top" >&nbsp;</div>
@ -541,7 +575,7 @@ Louis Lacoste
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@ -575,7 +609,7 @@ Louis Lacoste
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@ -607,7 +641,7 @@ Louis Lacoste
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@ -639,7 +673,7 @@ Louis Lacoste
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@ -669,7 +703,7 @@ Louis Lacoste
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@ -729,7 +763,7 @@ Louis Lacoste
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@ -759,7 +793,7 @@ Louis Lacoste
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1
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@ -2,6 +2,7 @@
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suivi/2025-13/2025-13.html
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2
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suivi/2025-17/2025-17.html
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<h1 class="title">Bilan semaine 35 2025 : 25 août - 29 août</h1>
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<div class="quarto-category">colBiSBM</div>
<div class="quarto-category">inférence</div>
<div class="quarto-category">GNN</div>
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<div class="quarto-title-meta-heading">Auteur·rice</div>
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<p class="author">Louis Lacoste <a href="mailto:louis.lacoste@agroparistech.fr" class="quarto-title-author-email"><i class="bi bi-envelope"></i></a> <a href="https://orcid.org/0009-0004-0178-9821" class="quarto-title-author-orcid"> <img src="data:image/png;base64,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"></a></p>
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<p class="affiliation">
MIA Paris-Saclay, INRAE, AgroParisTech, Université Paris-Saclay
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<p class="date">29 août 2025</p>
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<div class="quarto-title-meta-heading">Modifié</div>
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<p class="date-modified">26 août 2025</p>
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<section id="todo-list" class="level2">
<h2 class="anchored" data-anchor-id="todo-list">TODO List</h2>
<ul>
<li>Pour clustering de collections sur données <del>réelles</del> :<br>
→ L’intuition de Pierre semble être confirmé, les dissimilarités semblent arrêter de varier sensiblement pour de grandes valeurs <span class="math inline">(Q_1,Q_2)</span>.
<ul>
<li>❓Je n’arrive plus à reproduire le bug pour l’inférence…</li>
<li>😫 bug encore. S’assurer que ça marche et relancer</li>
</ul></li>
<li>⌛ A Roscoff avec Julie et Pierre nous avons constaté que c’était l’extraction des dyades pour le calcul des métriques qui était incorrecte. Maintenant c’est corrigé et ça fonctionne ! En fait je donne tous les degrés donc le GNN a juste à retrouver les arêtes non vues.Revérifier que j’entraîne correctement le VGAE car résultats de généralisation trop bons sur les autres réseaux Doré, ce qui est étonnant Pour corriger cet effet :
<ul>
<li>Donner la matrice identité comme features</li>
<li>Corriger les degrés calculés.</li>
</ul></li>
<li>Faible performances de l’inférence :
<ul>
<li>Vérifier que les conditions d’identifiabilité des modèles fautifs sont bien remplies.</li>
<li>Récupérer des jeux de paramètres et essayer de reproduire les résultats.</li>
</ul></li>
<li>Clustering sur Doré :
<ul>
<li>Regarder pour les couples date+nom les études et le nombre de réseaux analysables (Possible demander à Élisa)
<ul>
<li>⌛ Chamberlain et al semble intéressant à regarder ! Voir le Rmarkdown</li>
</ul></li>
<li>Clusteriser sur la base des noms et voir parmi les réseaux Européens (désagrégés ?)</li>
<li>Si M &gt; 10, alors voir si je retrouve les mêmes résultats que dans les études.</li>
</ul></li>
<li>Regarder les codes Mangal database pour <span class="math inline">\delta</span></li>
<li>Voir <span class="math inline">\delta</span> mais additif</li>
</ul>
<div class="callout callout-style-default callout-note callout-titled">
<div class="callout-header d-flex align-content-center">
<div class="callout-icon-container">
<i class="callout-icon"></i>
</div>
<div class="callout-title-container flex-fill">
<span class="math inline">\delta</span> additif Bernoulli
</div>
</div>
<div class="callout-body-container callout-body">
<p>En Bernoulli pas de forme analytique non plus : Pour <span class="math inline">\alpha_{qr}</span>: <span class="math display"> \sum_{m=1}^M \sum_{i=1}^{n_1^m} \sum_{j=1}^{n_2^m} \tau_{iq}^{1,m}\tau_{jr}^{2,m}(\frac{X_{ij}^m}{\alpha_{qr}} + \frac{(1-X_{ij}^m)}{\alpha_{qr} + \delta_m -1}) = 0</span> <span class="math display">\Leftrightarrow \sum_m \frac{e^m_{qr}}{\alpha_{qr}} + \frac{1}{\alpha_{qr}+\delta_m-1} (n^m_{qr}-e^m_{qr}) = 0</span></p>
<p>Et pour <span class="math inline">\delta_m</span>: <span class="math display"> \sum_{i=1}^{n_1^m} \sum_{j=1}^{n_2^m} \sum_{q=1}^{Q_1} \sum_{r=1}^{Q_2} \tau_{iq}^{1,m}\tau_{jr}^{2,m}(\frac{X_{ij}^m}{\delta_{m}} + \frac{(1-X_{ij}^m)}{\alpha_{qr} + \delta_m -1}) = 0</span></p>
</div>
</div>
<div class="callout callout-style-default callout-note callout-titled">
<div class="callout-header d-flex align-content-center">
<div class="callout-icon-container">
<i class="callout-icon"></i>
</div>
<div class="callout-title-container flex-fill">
<span class="math inline">\delta</span> additif Poisson
</div>
</div>
<div class="callout-body-container callout-body">
<p>Forme analytique mais risque de confusion ? <span class="math display">\widehat{\delta_m} = \frac{\sum_{q,r} e^m_{qr}}{\sum_{q,r} n^m_{qr}},~\widehat{\alpha_{qr}} = \frac{\sum_{m} e^m_{qr}}{\sum_{m} n^m_{qr}} </span></p>
</div>
</div>
<ul>
<li><p>Regarder la liste des cours du MathSV et de l’Université Paris-Saclay.</p></li>
<li><p>⌛ <strong>Plutôt regarder pour introduire un modèle <span class="math inline">\delta</span>-colBiSBM</strong>.</p>
<ul>
<li>Ajouter le produit par <span class="math inline">\delta</span> là où nécessaire</li>
<li>Ajouter les modèles <span class="math inline">\delta</span>, <span class="math inline">\delta\pi, \dots</span> et les blocs conditionnels</li>
<li>Ajouter les tests unitaires adéquats et les vérifier</li>
</ul></li>
<li><p>Regarder <em>Largest gap</em> sur réseaux Doré</p></li>
<li><p>Essayer <em>clustering</em> sur <code>supinfo</code></p></li>
<li><p>Homogénéiser notations dans les supplementaries</p></li>
</ul>
<div id="ref-kmeans-vae" class="quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-ref figure">
<div aria-describedby="ref-kmeans-vae-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<ul>
<li>Faire GNN-VAE Doré et sub-Doré avec kmeans et clustering sur l’espace latent J’ai commencé à regarder un peu</li>
</ul>
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-ref quarto-uncaptioned" id="ref-kmeans-vae-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Reference&nbsp;1
</figcaption>
</figure>
</div>
<section id="inférence-et-microbes" class="level3">
<h3 class="anchored" data-anchor-id="inférence-et-microbes">Inférence et microbes</h3>
<ul>
<li>easy16s : se renseigner sur
<ul>
<li><span class="math inline">\alpha</span>, <span class="math inline">\beta</span> diversité</li>
<li>Heatmap</li>
<li>Voir avec Mahendra à l’occasion du CSI</li>
</ul></li>
<li>Lancer <em>colBiSBM</em> sur <span class="math inline">OTU\times Sample</span> → problème du chargement en mémoire des données à voir</li>
<li>Lancer <em>colSBM</em> sur <span class="math inline">OTU\times OTU</span></li>
<li>TabNet pratiquer les <a href="https://github.com/cregouby/Tutoriel_torch">exercices</a></li>
<li>Regarder <strong>SPARTA</strong> Rennes</li>
<li>Lire Papiers compositional data (Aitchison et al.&nbsp;intro)</li>
<li>Lire article multi-niveaux Saint-Clair</li>
<li>Ecrire et étudier les modèles pour différents niveaux taxonomiques. <span class="math display">\begin{align*}
i \rightarrow &amp;~N^1_i \subseteq N^2_i \subseteq N^3_i &amp; \text{Taxonomie}\\
Z^0_i \overset{?}{=} &amp; Z^1_i \overset{?}{=} Z^2_i \overset{?}{=} Z^3_i &amp; \text{Groupes fonctionnels}
\end{align*}</span></li>
</ul>
<section id="causalité" class="level4">
<h4 class="anchored" data-anchor-id="causalité">Causalité</h4>
<p>Plus sur le temps long, à regarder</p>
<ul>
<li>GT causalité</li>
<li>Daria Bystrova lire présentation <span class="citation" data-cites="bystrovaCausalDiscovery">Bystrova (<a href="#ref-bystrovaCausalDiscovery" role="doc-biblioref">s.&nbsp;d.</a>)</span> (Meek rules, V-structure)</li>
</ul>
</section>
</section>
</section>
<section id="biblio-à-faire" class="level2">
<h2 class="anchored" data-anchor-id="biblio-à-faire">Biblio à faire</h2>
<ul>
<li>Regarder Transport optimal graphes bipartite.</li>
</ul>
</section>
<section id="lectures-en-cours" class="level2">
<h2 class="anchored" data-anchor-id="lectures-en-cours">Lectures en cours 📚</h2>
<section id="hdr-vincent-brault" class="level3">
<h3 class="anchored" data-anchor-id="hdr-vincent-brault">HDR Vincent Brault</h3>
<ul>
<li>⌛ Chap 2 : Creuser l’idée de maximiser l’énergie libre, très intéressant regarder le critère CARI et lire Robert et al 2021. Actuellement p32 du manuscrit</li>
<li>Chap 3</li>
</ul>
</section>
<section id="ot" class="level3">
<h3 class="anchored" data-anchor-id="ot">OT</h3>
<ul>
<li>⌛ <span class="citation" data-cites="mazeletUnsupervisedLearningOptimal">Mazelet, Flamary, et Thirion (<a href="#ref-mazeletUnsupervisedLearningOptimal" role="doc-biblioref">s.&nbsp;d.</a>)</span> Intéressant pour le transport optimal entre graphes de tailles différentes | Regarder si regularization entropique ne marche pas bien pour le graphe.</li>
<li>⌛ <span class="citation" data-cites="nennaLecture2Entropic">Nenna (<a href="#ref-nennaLecture2Entropic" role="doc-biblioref">s.&nbsp;d.b</a>)</span> Pour comprendre le problème d’OT régularisé pour l’entropie.</li>
<li>⌛ <span class="citation" data-cites="nennaLecture1Monge">Nenna (<a href="#ref-nennaLecture1Monge" role="doc-biblioref">s.&nbsp;d.a</a>)</span></li>
</ul>
</section>
<section id="inférence-de-graphes" class="level3">
<h3 class="anchored" data-anchor-id="inférence-de-graphes">Inférence de graphes</h3>
<ul>
<li><p>⌛ <span class="citation" data-cites="aitchisonStatisticalAnalysisCompositional1982a">Aitchison (<a href="#ref-aitchisonStatisticalAnalysisCompositional1982a" role="doc-biblioref">1982</a>)</span>, en cours</p></li>
<li><p>❗📖 <span class="citation" data-cites="payneFiniteMixturesMultivariate2023">Payne et al. (<a href="#ref-payneFiniteMixturesMultivariate2023" role="doc-biblioref">2023</a>)</span> sur MixMPLN</p></li>
</ul>
</section>
<section id="causalité-1" class="level3">
<h3 class="anchored" data-anchor-id="causalité-1">Causalité</h3>
<ul>
<li>❗📖 <span class="citation" data-cites="bystrovaCausalDiscovery">Bystrova (<a href="#ref-bystrovaCausalDiscovery" role="doc-biblioref">s.&nbsp;d.</a>)</span></li>
</ul>
</section>
<section id="largest-gaps" class="level3">
<h3 class="anchored" data-anchor-id="largest-gaps">Largest Gaps</h3>
<ul>
<li>❗📖 <span class="citation" data-cites="braultFastConsistentAlgorithm2023">Brault et Channarond (<a href="#ref-braultFastConsistentAlgorithm2023" role="doc-biblioref">2023</a>)</span></li>
<li>❗📖 <span class="citation" data-cites="channarondClassificationEstimationStochastic2012">Channarond, Daudin, et Robin (<a href="#ref-channarondClassificationEstimationStochastic2012" role="doc-biblioref">2012</a>)</span> le papier qui introduit le <em>Largest Gaps</em></li>
</ul>
</section>
</section>
<section id="a-discuter" class="level2">
<h2 class="anchored" data-anchor-id="a-discuter">A discuter</h2>
<section id="congés-ps" class="level3">
<h3 class="anchored" data-anchor-id="congés-ps">Congés P&amp;S</h3>
</section>
<section id="thèse" class="level3">
<h3 class="anchored" data-anchor-id="thèse">Thèse</h3>
<ul>
<li>Faire préz CSI</li>
<li>Faire rapport CSI</li>
</ul>
</section>
<section id="inférence" class="level3">
<h3 class="anchored" data-anchor-id="inférence">Inférence</h3>
<ul>
<li>pbs : variance, bcp de zero, covariables, offset et taxonomie (Reseaux arretes differents niveaux : Genre, OTU …)</li>
</ul>
<blockquote class="blockquote">
<p>Combine networks at different taxonomic levels</p>
</blockquote>
<ul>
<li>Inférence + GREMLINS</li>
</ul>
</section>
</section>
<div id="quarto-appendix" class="default"><section class="quarto-appendix-contents" role="doc-bibliography" id="quarto-bibliography"><h2 class="anchored quarto-appendix-heading">Les références</h2><div id="refs" class="references csl-bib-body hanging-indent" data-entry-spacing="0" role="list">
<div id="ref-aitchisonStatisticalAnalysisCompositional1982a" class="csl-entry" role="listitem">
Aitchison, J. 1982. <span>«&nbsp;The <span>Statistical Analysis</span> of <span>Compositional Data</span>&nbsp;»</span>. <em>Journal of the Royal Statistical Society. Series B (Methodological)</em> 44 (2): 139‑77. <a href="https://www.jstor.org/stable/2345821">https://www.jstor.org/stable/2345821</a>.
</div>
<div id="ref-braultFastConsistentAlgorithm2023" class="csl-entry" role="listitem">
Brault, Vincent, et Antoine Channarond. 2023. <span>«&nbsp;Fast and <span>Consistent Algorithm</span> for the <span>Latent Block Model</span>&nbsp;»</span>. 9 mars 2023. <a href="https://doi.org/10.48550/arXiv.1610.09005">https://doi.org/10.48550/arXiv.1610.09005</a>.
</div>
<div id="ref-bystrovaCausalDiscovery" class="csl-entry" role="listitem">
Bystrova, Daria. s.&nbsp;d. <span>«&nbsp;Causal Discovery&nbsp;»</span>.
</div>
<div id="ref-channarondClassificationEstimationStochastic2012" class="csl-entry" role="listitem">
Channarond, Antoine, Jean-Jacques Daudin, et Stéphane Robin. 2012. <span>«&nbsp;Classification and Estimation in the <span>Stochastic Blockmodel</span> Based on the Empirical Degrees&nbsp;»</span>. <em>Electronic Journal of Statistics</em> 6 (janvier). <a href="https://doi.org/10.1214/12-ejs753">https://doi.org/10.1214/12-ejs753</a>.
</div>
<div id="ref-mazeletUnsupervisedLearningOptimal" class="csl-entry" role="listitem">
Mazelet, Sonia, Rémi Flamary, et Bertrand Thirion. s.&nbsp;d. <span>«&nbsp;Unsupervised <span>Learning</span> for <span>Optimal Transport</span> Plan Prediction Between Unbalanced Graphs&nbsp;»</span>.
</div>
<div id="ref-nennaLecture1Monge" class="csl-entry" role="listitem">
Nenna, Luca. s.&nbsp;d.a. <span>«&nbsp;Lecture 1 <span>Monge</span> and <span>Kantorovich</span> Problems: From Primal to Dual&nbsp;»</span>.
</div>
<div id="ref-nennaLecture2Entropic" class="csl-entry" role="listitem">
———. s.&nbsp;d.b. <span>«&nbsp;Lecture 2: <span>Entropic Optimal Transport</span>&nbsp;»</span>.
</div>
<div id="ref-payneFiniteMixturesMultivariate2023" class="csl-entry" role="listitem">
Payne, Andrea, Anjali Silva, Steven J. Rothstein, Paul D. McNicholas, et Sanjeena Subedi. 2023. <span>«&nbsp;Finite <span>Mixtures</span> of <span>Multivariate Poisson-Log Normal Factor Analyzers</span> for <span>Clustering Count Data</span>&nbsp;»</span>. 13 novembre 2023. <a href="https://doi.org/10.48550/arXiv.2311.07762">https://doi.org/10.48550/arXiv.2311.07762</a>.
</div>
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