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<h1 class="title">Bilan semaine 9 2026 : 16 mars - 20 mars</h1>
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<div class="quarto-categories">
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<div class="quarto-category">colBiSBM</div>
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<div class="quarto-category">inférence</div>
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<div class="quarto-category">GNN</div>
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<div class="quarto-title-meta-heading">Auteur·rice</div>
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<div class="quarto-title-meta-heading">Affiliation</div>
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<div class="quarto-title-meta-contents">
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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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</div>
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<div class="quarto-title-meta-contents">
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<p class="affiliation">
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MIA Paris-Saclay, INRAE, AgroParisTech, Université Paris-Saclay
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<div class="quarto-title-meta-heading">Date de publication</div>
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<p class="date">16 mars 2026</p>
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<div>
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<div class="quarto-title-meta-heading">Modifié</div>
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<div class="quarto-title-meta-contents">
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<p class="date-modified">7 mai 2026</p>
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</div>
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<h2 id="toc-title">Sur cette page</h2>
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<ul>
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<li><a href="#todo-list" id="toc-todo-list" class="nav-link active" data-scroll-target="#todo-list"><span class="header-section-number">1</span> TODO List</a>
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<ul class="collapse">
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<li><a href="#mes-priorités-de-la-semaine" id="toc-mes-priorités-de-la-semaine" class="nav-link" data-scroll-target="#mes-priorités-de-la-semaine"><span class="header-section-number">1.1</span> Mes priorités de la semaine</a></li>
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<li><a href="#les-autres-tâches" id="toc-les-autres-tâches" class="nav-link" data-scroll-target="#les-autres-tâches"><span class="header-section-number">1.2</span> Les autres tâches</a></li>
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<li><a href="#inférence-et-microbes" id="toc-inférence-et-microbes" class="nav-link" data-scroll-target="#inférence-et-microbes"><span class="header-section-number">1.3</span> Inférence et microbes</a></li>
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</ul></li>
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<li><a href="#a-discuter" id="toc-a-discuter" class="nav-link" data-scroll-target="#a-discuter"><span class="header-section-number">2</span> A discuter</a></li>
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<li><a href="#biblio-à-faire" id="toc-biblio-à-faire" class="nav-link" data-scroll-target="#biblio-à-faire"><span class="header-section-number">3</span> Biblio à faire</a></li>
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<li><a href="#lectures-en-cours" id="toc-lectures-en-cours" class="nav-link" data-scroll-target="#lectures-en-cours"><span class="header-section-number">4</span> Lectures en cours 📚</a>
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<ul class="collapse">
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<li><a href="#hdr-vincent-brault" id="toc-hdr-vincent-brault" class="nav-link" data-scroll-target="#hdr-vincent-brault"><span class="header-section-number">4.1</span> HDR Vincent Brault</a></li>
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<li><a href="#ot" id="toc-ot" class="nav-link" data-scroll-target="#ot"><span class="header-section-number">4.2</span> OT</a></li>
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<li><a href="#inférence-de-graphes" id="toc-inférence-de-graphes" class="nav-link" data-scroll-target="#inférence-de-graphes"><span class="header-section-number">4.3</span> Inférence de graphes</a></li>
|
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<li><a href="#causalité-1" id="toc-causalité-1" class="nav-link" data-scroll-target="#causalité-1"><span class="header-section-number">4.4</span> Causalité</a></li>
|
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<li><a href="#largest-gaps" id="toc-largest-gaps" class="nav-link" data-scroll-target="#largest-gaps"><span class="header-section-number">4.5</span> Largest Gaps</a></li>
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</ul></li>
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<section id="todo-list" class="level2" data-number="1">
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<h2 data-number="1" class="anchored" data-anchor-id="todo-list"><span class="header-section-number">1</span> TODO List</h2>
|
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<section id="mes-priorités-de-la-semaine" class="level3" data-number="1.1">
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||
<h3 data-number="1.1" class="anchored" data-anchor-id="mes-priorités-de-la-semaine"><span class="header-section-number">1.1</span> Mes priorités de la semaine</h3>
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||
<ul>
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||
<li>Faire tourner clustering colBiSBM sur les clusters dégagés par Mona et l’accompagner sur la rédaction de son poster</li>
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||
<li>Préparer ma présentation (voir le bloc ci-après) pour Rochebrune et donner un titre :
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||
<ul>
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<li>“Comparing networks, a challenging task?” (NUL/20)</li>
|
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<li>“High and low: comparing networks, <del>a burden for the mind</del> what works and what don’t(?)”</li>
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</ul></li>
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<li>Finir implémentation dans sbm de blockmodels avec covariables sur les noeuds</li>
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<li>Bricoler une pipeline sbm |> nnet::multinom comme performance de référence pour l’introduction de covariables</li>
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||
<li>Comprendre pourquoi l’idée géniale de Sophie est remise en question</li>
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||
<li>Corriger les copies des 1As</li>
|
||
</ul>
|
||
<div class="callout callout-style-default callout-note callout-titled" title="Idées présentation Rochebrune">
|
||
<div class="callout-header d-flex align-content-center">
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<div class="callout-icon-container">
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<i class="callout-icon"></i>
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</div>
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<div class="callout-title-container flex-fill">
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Idées présentation Rochebrune
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</div>
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</div>
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<div class="callout-body-container callout-body">
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<ul>
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<li>colBiSBM: ce qui marche (et à la fin la galère du clustering)</li>
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<li>Transition sur les OTUs et motivations de pourquoi c’est galère (#OTU>>#Sample, dépendance par la phylogénie …)</li>
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<li>Motivation du co-clustering (LBM), trouver des groupes d’échantillons et d’OTUs qui exhibent des comportement différents (pathologies, sols particuliers, échantillon alimentaire avec une flore d’intérêt …)</li>
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<li>Première idée: LBM séquentiel, faire repartir des <span class="math inline">\tau^{l}</span> pour initialiser les <span class="math inline">\tau^{l+1}</span> selon l’arbre phylogénétique
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<ul>
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<li>Idées: faciliter l’exploration du paysage de l’ELBO en se plaçant dans une région de paramètres qui a du sens phylo et donc en sortir indiquerait un signal dans les données.</li>
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<li>Problèmes: ne résout pas le problème de la dimensionnalité en les OTUs</li>
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<li>Comme implém., performances pas incroyable et signal peu clair.</li>
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</ul></li>
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<li>Deuxième idée: SBM et LBM avec covariables sur les noeuds. Reconstruire des positions dans un espace phylogénétique à partir des matrices de distances phylogénétique (et donc en accord avec l’arbre). Mais aussi modèle plus large pour prendre en compte diverses situations (trouver des exemples d’autres données?).
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<ul>
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<li>Idées: possible de former les groupes a priori selon les tendances dans les covariables (reflet de la phylogénie) et mettre à jour selon les données</li>
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<li>Quasiment implémenté dans <code>{blockmodels}</code> et dans <code>{sbm}</code> (j’aimerai pouvoir dire le jour de ma présentation que c’est dispo sur la version de développement, il va falloir charbonner de mon côté).</li>
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<li>Théoriquement: on a l’identifiabilité (ou pas vu la pratique?)</li>
|
||
<li>Problèmes: Ne résoud pas les problèmes de calculs, en pratique on ne retrouve pas les bons coefficients (label-switching?)</li>
|
||
</ul></li>
|
||
<li>Troisième idée: Utiliser la structure de l’arbre phylogénétique pour encoder une relation des positions latentes dans un <em>Latent Position Model</em> (LPM).
|
||
<ul>
|
||
<li>Détail: pour chaque couche <span class="math inline">l</span>, chaque individu de la couche <span class="math inline">i</span> et son ancêtre <span class="math inline">j=Ancestor(i)</span> (CITER LES PAPIERS A L’ORIGINE DE L’IDEE), on écrit <span class="math inline">\gamma_{l,i} = \gamma_{l-1,j=Ancestor(i)} + \delta_{l,i} = \gamma_{0} + \sum_{k\in Ancestry(i)} \delta_{l,k}</span> (puisque qu’on a un unique ancêtre dans chaque couche <span class="math inline">l</span>), les noeuds qui partagent un ancêtre commun partage la position latente à ce niveau et lui ajoute un décalage <span class="math inline">\delta</span>.</li>
|
||
<li>Remarque: Peut-être possible de gérer les cas de transferts horizontaux en élargissant le concept de lignée ancestrale?</li>
|
||
<li>Idée: (casquette de biologiste) possible d’avoir dans une même lignée phylogénétique des individus qui se spécialisent voire qui développent une convergence évolutive et acquièrent des traits phénotypiques qui ressemblent à d’autres familles. Dans ce cas, le <span class="math inline">\delta_{l,i}</span> rapprochent le <span class="math inline">\gamma_{l,i}</span> d’un <span class="math inline">\gamma_{l, i^{\prime}}</span> qui a le trait commun.</li>
|
||
<li>Problème: les calculs seraient ils simplifiés? pas sûr. Et je n’y ai pas encore touché.</li>
|
||
</ul></li>
|
||
<li>Utiliser les Hierarchical SBM et LBM de Peixoto dans son package <code>graphtools</code> pour initialiser l’arbre liant les couches avec l’arbre phylogénétique.</li>
|
||
</ul>
|
||
</div>
|
||
</div>
|
||
</section>
|
||
<section id="les-autres-tâches" class="level3" data-number="1.2">
|
||
<h3 data-number="1.2" class="anchored" data-anchor-id="les-autres-tâches"><span class="header-section-number">1.2</span> Les autres tâches</h3>
|
||
<ul>
|
||
<li>Petites opérations sur les OTUs (regarder la matrice dans les yeux):
|
||
<ul>
|
||
<li>Ranger les OTUs par variances (i.e. <code>sd(OTU_j)</code>)
|
||
<ul>
|
||
<li>HMC sur-dispersés (au-dessus bissectrice)</li>
|
||
<li>Enterotype phyloseq sous-disp</li>
|
||
</ul></li>
|
||
<li>Regarder la proportion de 1. taxon rares, 2. zeros.</li>
|
||
<li>Faire des coupures selon niveaux taxonomiques et regarder si <span class="math inline">\mathbb{V}_{\text{intra}} \approx \mathbb{V}_{\text{inter}}</span></li>
|
||
<li><em>Bonus</em>: faire ça dans qmd et voir si forge permet gitlab pages</li>
|
||
</ul></li>
|
||
<li>Relire <span class="citation" data-cites="peixotoHierarchicalBlockStructures2014">Peixoto (<a href="#ref-peixotoHierarchicalBlockStructures2014" role="doc-biblioref">2014</a>)</span>
|
||
<ul>
|
||
<li>Regarder les gens qui citent les travaux de Peixoto</li>
|
||
<li>Utiliser graphtools en initialisant la recherche Nested avec le partitionnement donné par l’arbre phylogénétique.</li>
|
||
</ul></li>
|
||
</ul>
|
||
<div class="callout callout-style-default callout-note callout-titled" title="Idées">
|
||
<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">
|
||
Idées
|
||
</div>
|
||
</div>
|
||
<div class="callout-body-container callout-body">
|
||
<ul>
|
||
<li>Trouver manière de faire un compromis : <span class="math inline">\ell(Y,Z,W;\theta) - \lambda d(C(W),C_0)</span> avec <span class="math inline">C(W)</span> le clustering seulement sur la base de la structure LBM et <span class="math inline">C_0</span> le clustering de l’arbre. Problème <span class="math inline">d</span> est une distance entre partition, comment optimiser dessus ?</li>
|
||
<li>⌛ Mise à jour partielle des <span class="math inline">\tau</span> : ce qui pose soucis c’est les gros calculs matriciels (c’est vraiment vrai?). Donc sorte de “stochastic” VEM où on update seulement une partie des <span class="math inline">\tau</span> à chaque itération. Et échantillonnage stratifié selon l’arbre ?
|
||
<ul>
|
||
<li>⌛ Simulations avec <span class="math inline">n_2</span> croissant lancée sur Migale</li>
|
||
<li>Réimplementé VE Bernoulli dans colSBM pour Bipartite et début implémentation Stochastic VE. En fait le problème des calculs matriciels <span class="math inline">Y\times(\tau^{(1)})^{\top}</span> (<span class="math inline">n_2^2</span>) donc besoin de sous-échantillonner les noeuds de l’autre dimension à mettre à jour.</li>
|
||
</ul></li>
|
||
</ul>
|
||
</div>
|
||
</div>
|
||
<ul>
|
||
<li><p>Clustering unipartite j’ai cassé une fonction de distance à vérifier et réparer</p></li>
|
||
<li><p>Codes pour le papier :</p>
|
||
<ul>
|
||
<li>Nettoyer les scripts</li>
|
||
<li>Faire un joli README</li>
|
||
<li>❓Faire des notebooks</li>
|
||
</ul></li>
|
||
<li><p>Réussir à reproduire résultat de <span class="citation" data-cites="abramovStructureKnowsBest">Abramov et al. (<a href="#ref-abramovStructureKnowsBest" role="doc-biblioref">s. d.</a>)</span></p></li>
|
||
<li><p>Maitriser graphtools de Peixoto pour essayer d’utiliser l’arbre taxonomique sur graphe de cooccurence inférer par SparCC</p></li>
|
||
<li><p>Maitriser SparCC</p></li>
|
||
<li><p>👶 (délégué à Mona) Clustering sur Doré :</p>
|
||
<ul>
|
||
<li>Ajouter Chao1 et 2, colonne par colonne (site par site), et faire indice moyen et la variance.</li>
|
||
</ul></li>
|
||
</ul>
|
||
</section>
|
||
<section id="inférence-et-microbes" class="level3" data-number="1.3">
|
||
<h3 data-number="1.3" class="anchored" data-anchor-id="inférence-et-microbes"><span class="header-section-number">1.3</span> Inférence et microbes</h3>
|
||
<ul>
|
||
<li>⌛ (En cours) Possible en modifiant lbm.h et sbm.h d’obtenir un modèle utilisant les covariables de groupes (de blocs ?). Car besoin de changer <code>membership.m_step()</code> pour mettre à jour <span class="math inline">\pmb\pi</span> et <span class="math inline">\pmb{\rho}</span> en utilisant les <span class="math inline">\pmb B^{\top}\pmb X</span> et en renvoyant l’ELBO adaptée.
|
||
<ul>
|
||
<li><p>😄 Avantage s’inscrit directement dans blockmodels et permet d’avoir toutes les lois d’émissions déjà codées et compatibles !</p></li>
|
||
<li><p>😢 Besoin de réfléchir a une bonne implémentation.</p></li>
|
||
</ul></li>
|
||
</ul>
|
||
<p>J’ai codé l’optimisation et les transferts mais il faut que je vérifie que tout fonctionne</p>
|
||
<ul>
|
||
<li>✅ Appliqué multipartite sur <span class="math inline">\forall i, OTU_i \times Sample</span>: <img src="figs/Multipartite.svg" class="img-fluid" alt="Le plot des groupes trouvés par le multipartite (2 pour tous les OTUs et 4 pour les échantillons.)"></li>
|
||
</ul>
|
||
<section id="bibliographie-à-lire-à-faire" class="level4" data-number="1.3.1">
|
||
<h4 data-number="1.3.1" class="anchored" data-anchor-id="bibliographie-à-lire-à-faire"><span class="header-section-number">1.3.1</span> Bibliographie: à lire, à faire</h4>
|
||
<ul>
|
||
<li>Lire article multi-niveaux Saint-Clair</li>
|
||
<li>🆕 🔎 Trouver des papiers:
|
||
<ul>
|
||
<li>LBM Negative Binomial</li>
|
||
<li>Network inference through sample comparison</li>
|
||
</ul></li>
|
||
<li>Idée des groupes sur la base de distance phylogénétique:
|
||
<ul>
|
||
<li>En train de comprendre les distances que phyloseq permet de calculer sur notre exemple</li>
|
||
<li>En train de lire sur Principle coordinate analysis : https://openplantpathology.github.io/OPP_Workshop_Multivariate/2-MV_PCO.html</li>
|
||
<li>Parametric t-SNE pour avoir une unique représentation latente (inconvénient utilise du Deep Learning)</li>
|
||
<li>Lire Papier UniFrac</li>
|
||
</ul></li>
|
||
</ul>
|
||
</section>
|
||
<section id="écrire-et-faire-tourner" class="level4" data-number="1.3.2">
|
||
<h4 data-number="1.3.2" class="anchored" data-anchor-id="écrire-et-faire-tourner"><span class="header-section-number">1.3.2</span> Écrire et faire tourner</h4>
|
||
<ul>
|
||
<li>🆕 SparCC à différent niveaux</li>
|
||
<li>🆕⌛ Tree-PLN à différents niveaux</li>
|
||
</ul>
|
||
</section>
|
||
<section id="causalité" class="level4" data-number="1.3.3">
|
||
<h4 data-number="1.3.3" class="anchored" data-anchor-id="causalité"><span class="header-section-number">1.3.3</span> 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. d.</a>)</span> (Meek rules, V-structure)</li>
|
||
</ul>
|
||
</section>
|
||
</section>
|
||
</section>
|
||
<section id="a-discuter" class="level2" data-number="2">
|
||
<h2 data-number="2" class="anchored" data-anchor-id="a-discuter"><span class="header-section-number">2</span> A discuter</h2>
|
||
<ul>
|
||
<li>🆕 Voir pour des Réseaux / GDR ou aller</li>
|
||
<li>🆕 Chercher des cours à suivre</li>
|
||
</ul>
|
||
</section>
|
||
<section id="biblio-à-faire" class="level2" data-number="3">
|
||
<h2 data-number="3" class="anchored" data-anchor-id="biblio-à-faire"><span class="header-section-number">3</span> Biblio à faire</h2>
|
||
<ul>
|
||
<li>Regarder Transport optimal graphes bipartite.</li>
|
||
</ul>
|
||
</section>
|
||
<section id="lectures-en-cours" class="level2" data-number="4">
|
||
<h2 data-number="4" class="anchored" data-anchor-id="lectures-en-cours"><span class="header-section-number">4</span> Lectures en cours 📚</h2>
|
||
<section id="hdr-vincent-brault" class="level3" data-number="4.1">
|
||
<h3 data-number="4.1" class="anchored" data-anchor-id="hdr-vincent-brault"><span class="header-section-number">4.1</span> 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" data-number="4.2">
|
||
<h3 data-number="4.2" class="anchored" data-anchor-id="ot"><span class="header-section-number">4.2</span> OT</h3>
|
||
<ul>
|
||
<li>⌛ <span class="citation" data-cites="mazeletUnsupervisedLearningOptimal">Mazelet, Flamary, et Thirion (<a href="#ref-mazeletUnsupervisedLearningOptimal" role="doc-biblioref">s. 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. 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. d.a</a>)</span></li>
|
||
</ul>
|
||
</section>
|
||
<section id="inférence-de-graphes" class="level3" data-number="4.3">
|
||
<h3 data-number="4.3" class="anchored" data-anchor-id="inférence-de-graphes"><span class="header-section-number">4.3</span> 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" data-number="4.4">
|
||
<h3 data-number="4.4" class="anchored" data-anchor-id="causalité-1"><span class="header-section-number">4.4</span> Causalité</h3>
|
||
<ul>
|
||
<li>❗📖 <span class="citation" data-cites="bystrovaCausalDiscovery">Bystrova (<a href="#ref-bystrovaCausalDiscovery" role="doc-biblioref">s. d.</a>)</span></li>
|
||
</ul>
|
||
</section>
|
||
<section id="largest-gaps" class="level3" data-number="4.5">
|
||
<h3 data-number="4.5" class="anchored" data-anchor-id="largest-gaps"><span class="header-section-number">4.5</span> 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>
|
||
|
||
<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-abramovStructureKnowsBest" class="csl-entry" role="listitem">
|
||
Abramov, Kesem, Barry Biton, Geut Galai, Rami Puzis, et Shai Pilosof. s. d. <span>« Structure Knows Best: Predicting Ecological Interactions Across Space Through Pairwise Integration of Latent Network Patterns »</span>.
|
||
</div>
|
||
<div id="ref-aitchisonStatisticalAnalysisCompositional1982a" class="csl-entry" role="listitem">
|
||
Aitchison, J. 1982. <span>« The <span>Statistical Analysis</span> of <span>Compositional Data</span> »</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>« Fast and <span>Consistent Algorithm</span> for the <span>Latent Block Model</span> »</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. d. <span>« Causal Discovery »</span>.
|
||
</div>
|
||
<div id="ref-channarondClassificationEstimationStochastic2012" class="csl-entry" role="listitem">
|
||
Channarond, Antoine, Jean-Jacques Daudin, et Stéphane Robin. 2012. <span>« Classification and Estimation in the <span>Stochastic Blockmodel</span> Based on the Empirical Degrees »</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. d. <span>« Unsupervised <span>Learning</span> for <span>Optimal Transport</span> Plan Prediction Between Unbalanced Graphs »</span>.
|
||
</div>
|
||
<div id="ref-nennaLecture1Monge" class="csl-entry" role="listitem">
|
||
Nenna, Luca. s. d.a. <span>« Lecture 1 <span>Monge</span> and <span>Kantorovich</span> Problems: From Primal to Dual »</span>.
|
||
</div>
|
||
<div id="ref-nennaLecture2Entropic" class="csl-entry" role="listitem">
|
||
———. s. d.b. <span>« Lecture 2: <span>Entropic Optimal Transport</span> »</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>« Finite <span>Mixtures</span> of <span>Multivariate Poisson-Log Normal Factor Analyzers</span> for <span>Clustering Count Data</span> »</span>. 13 novembre 2023. <a href="https://doi.org/10.48550/arXiv.2311.07762">https://doi.org/10.48550/arXiv.2311.07762</a>.
|
||
</div>
|
||
<div id="ref-peixotoHierarchicalBlockStructures2014" class="csl-entry" role="listitem">
|
||
Peixoto, Tiago P. 2014. <span>« Hierarchical <span>Block Structures</span> and <span>High-Resolution Model Selection</span> in <span>Large Networks</span> »</span>. <em>Physical Review X</em> 4 (1): 011047. <a href="https://doi.org/10.1103/PhysRevX.4.011047">https://doi.org/10.1103/PhysRevX.4.011047</a>.
|
||
</div>
|
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if (window.document.getElementById('quarto-embedded-source-code-modal')) {
|
||
const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
|
||
text: getTextToCopy,
|
||
container: window.document.getElementById('quarto-embedded-source-code-modal')
|
||
});
|
||
clipboardModal.on('success', onCopySuccess);
|
||
}
|
||
var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
|
||
var mailtoRegex = new RegExp(/^mailto:/);
|
||
var filterRegex = new RegExp('/' + window.location.host + '/');
|
||
var isInternal = (href) => {
|
||
return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
|
||
}
|
||
// Inspect non-navigation links and adorn them if external
|
||
var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
|
||
for (var i=0; i<links.length; i++) {
|
||
const link = links[i];
|
||
if (!isInternal(link.href)) {
|
||
// undo the damage that might have been done by quarto-nav.js in the case of
|
||
// links that we want to consider external
|
||
if (link.dataset.originalHref !== undefined) {
|
||
link.href = link.dataset.originalHref;
|
||
}
|
||
}
|
||
}
|
||
function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
|
||
const config = {
|
||
allowHTML: true,
|
||
maxWidth: 500,
|
||
delay: 100,
|
||
arrow: false,
|
||
appendTo: function(el) {
|
||
return el.parentElement;
|
||
},
|
||
interactive: true,
|
||
interactiveBorder: 10,
|
||
theme: 'quarto',
|
||
placement: 'bottom-start',
|
||
};
|
||
if (contentFn) {
|
||
config.content = contentFn;
|
||
}
|
||
if (onTriggerFn) {
|
||
config.onTrigger = onTriggerFn;
|
||
}
|
||
if (onUntriggerFn) {
|
||
config.onUntrigger = onUntriggerFn;
|
||
}
|
||
window.tippy(el, config);
|
||
}
|
||
const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]');
|
||
for (var i=0; i<noterefs.length; i++) {
|
||
const ref = noterefs[i];
|
||
tippyHover(ref, function() {
|
||
// use id or data attribute instead here
|
||
let href = ref.getAttribute('data-footnote-href') || ref.getAttribute('href');
|
||
try { href = new URL(href).hash; } catch {}
|
||
const id = href.replace(/^#\/?/, "");
|
||
const note = window.document.getElementById(id);
|
||
if (note) {
|
||
return note.innerHTML;
|
||
} else {
|
||
return "";
|
||
}
|
||
});
|
||
}
|
||
const xrefs = window.document.querySelectorAll('a.quarto-xref');
|
||
const processXRef = (id, note) => {
|
||
// Strip column container classes
|
||
const stripColumnClz = (el) => {
|
||
el.classList.remove("page-full", "page-columns");
|
||
if (el.children) {
|
||
for (const child of el.children) {
|
||
stripColumnClz(child);
|
||
}
|
||
}
|
||
}
|
||
stripColumnClz(note)
|
||
if (id === null || id.startsWith('sec-')) {
|
||
// Special case sections, only their first couple elements
|
||
const container = document.createElement("div");
|
||
if (note.children && note.children.length > 2) {
|
||
container.appendChild(note.children[0].cloneNode(true));
|
||
for (let i = 1; i < note.children.length; i++) {
|
||
const child = note.children[i];
|
||
if (child.tagName === "P" && child.innerText === "") {
|
||
continue;
|
||
} else {
|
||
container.appendChild(child.cloneNode(true));
|
||
break;
|
||
}
|
||
}
|
||
if (window.Quarto?.typesetMath) {
|
||
window.Quarto.typesetMath(container);
|
||
}
|
||
return container.innerHTML
|
||
} else {
|
||
if (window.Quarto?.typesetMath) {
|
||
window.Quarto.typesetMath(note);
|
||
}
|
||
return note.innerHTML;
|
||
}
|
||
} else {
|
||
// Remove any anchor links if they are present
|
||
const anchorLink = note.querySelector('a.anchorjs-link');
|
||
if (anchorLink) {
|
||
anchorLink.remove();
|
||
}
|
||
if (window.Quarto?.typesetMath) {
|
||
window.Quarto.typesetMath(note);
|
||
}
|
||
if (note.classList.contains("callout")) {
|
||
return note.outerHTML;
|
||
} else {
|
||
return note.innerHTML;
|
||
}
|
||
}
|
||
}
|
||
for (var i=0; i<xrefs.length; i++) {
|
||
const xref = xrefs[i];
|
||
tippyHover(xref, undefined, function(instance) {
|
||
instance.disable();
|
||
let url = xref.getAttribute('href');
|
||
let hash = undefined;
|
||
if (url.startsWith('#')) {
|
||
hash = url;
|
||
} else {
|
||
try { hash = new URL(url).hash; } catch {}
|
||
}
|
||
if (hash) {
|
||
const id = hash.replace(/^#\/?/, "");
|
||
const note = window.document.getElementById(id);
|
||
if (note !== null) {
|
||
try {
|
||
const html = processXRef(id, note.cloneNode(true));
|
||
instance.setContent(html);
|
||
} finally {
|
||
instance.enable();
|
||
instance.show();
|
||
}
|
||
} else {
|
||
// See if we can fetch this
|
||
fetch(url.split('#')[0])
|
||
.then(res => res.text())
|
||
.then(html => {
|
||
const parser = new DOMParser();
|
||
const htmlDoc = parser.parseFromString(html, "text/html");
|
||
const note = htmlDoc.getElementById(id);
|
||
if (note !== null) {
|
||
const html = processXRef(id, note);
|
||
instance.setContent(html);
|
||
}
|
||
}).finally(() => {
|
||
instance.enable();
|
||
instance.show();
|
||
});
|
||
}
|
||
} else {
|
||
// See if we can fetch a full url (with no hash to target)
|
||
// This is a special case and we should probably do some content thinning / targeting
|
||
fetch(url)
|
||
.then(res => res.text())
|
||
.then(html => {
|
||
const parser = new DOMParser();
|
||
const htmlDoc = parser.parseFromString(html, "text/html");
|
||
const note = htmlDoc.querySelector('main.content');
|
||
if (note !== null) {
|
||
// This should only happen for chapter cross references
|
||
// (since there is no id in the URL)
|
||
// remove the first header
|
||
if (note.children.length > 0 && note.children[0].tagName === "HEADER") {
|
||
note.children[0].remove();
|
||
}
|
||
const html = processXRef(null, note);
|
||
instance.setContent(html);
|
||
}
|
||
}).finally(() => {
|
||
instance.enable();
|
||
instance.show();
|
||
});
|
||
}
|
||
}, function(instance) {
|
||
});
|
||
}
|
||
let selectedAnnoteEl;
|
||
const selectorForAnnotation = ( cell, annotation) => {
|
||
let cellAttr = 'data-code-cell="' + cell + '"';
|
||
let lineAttr = 'data-code-annotation="' + annotation + '"';
|
||
const selector = 'span[' + cellAttr + '][' + lineAttr + ']';
|
||
return selector;
|
||
}
|
||
const selectCodeLines = (annoteEl) => {
|
||
const doc = window.document;
|
||
const targetCell = annoteEl.getAttribute("data-target-cell");
|
||
const targetAnnotation = annoteEl.getAttribute("data-target-annotation");
|
||
const annoteSpan = window.document.querySelector(selectorForAnnotation(targetCell, targetAnnotation));
|
||
const lines = annoteSpan.getAttribute("data-code-lines").split(",");
|
||
const lineIds = lines.map((line) => {
|
||
return targetCell + "-" + line;
|
||
})
|
||
let top = null;
|
||
let height = null;
|
||
let parent = null;
|
||
if (lineIds.length > 0) {
|
||
//compute the position of the single el (top and bottom and make a div)
|
||
const el = window.document.getElementById(lineIds[0]);
|
||
top = el.offsetTop;
|
||
height = el.offsetHeight;
|
||
parent = el.parentElement.parentElement;
|
||
if (lineIds.length > 1) {
|
||
const lastEl = window.document.getElementById(lineIds[lineIds.length - 1]);
|
||
const bottom = lastEl.offsetTop + lastEl.offsetHeight;
|
||
height = bottom - top;
|
||
}
|
||
if (top !== null && height !== null && parent !== null) {
|
||
// cook up a div (if necessary) and position it
|
||
let div = window.document.getElementById("code-annotation-line-highlight");
|
||
if (div === null) {
|
||
div = window.document.createElement("div");
|
||
div.setAttribute("id", "code-annotation-line-highlight");
|
||
div.style.position = 'absolute';
|
||
parent.appendChild(div);
|
||
}
|
||
div.style.top = top - 2 + "px";
|
||
div.style.height = height + 4 + "px";
|
||
div.style.left = 0;
|
||
let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter");
|
||
if (gutterDiv === null) {
|
||
gutterDiv = window.document.createElement("div");
|
||
gutterDiv.setAttribute("id", "code-annotation-line-highlight-gutter");
|
||
gutterDiv.style.position = 'absolute';
|
||
const codeCell = window.document.getElementById(targetCell);
|
||
const gutter = codeCell.querySelector('.code-annotation-gutter');
|
||
gutter.appendChild(gutterDiv);
|
||
}
|
||
gutterDiv.style.top = top - 2 + "px";
|
||
gutterDiv.style.height = height + 4 + "px";
|
||
}
|
||
selectedAnnoteEl = annoteEl;
|
||
}
|
||
};
|
||
const unselectCodeLines = () => {
|
||
const elementsIds = ["code-annotation-line-highlight", "code-annotation-line-highlight-gutter"];
|
||
elementsIds.forEach((elId) => {
|
||
const div = window.document.getElementById(elId);
|
||
if (div) {
|
||
div.remove();
|
||
}
|
||
});
|
||
selectedAnnoteEl = undefined;
|
||
};
|
||
// Handle positioning of the toggle
|
||
window.addEventListener(
|
||
"resize",
|
||
throttle(() => {
|
||
elRect = undefined;
|
||
if (selectedAnnoteEl) {
|
||
selectCodeLines(selectedAnnoteEl);
|
||
}
|
||
}, 10)
|
||
);
|
||
function throttle(fn, ms) {
|
||
let throttle = false;
|
||
let timer;
|
||
return (...args) => {
|
||
if(!throttle) { // first call gets through
|
||
fn.apply(this, args);
|
||
throttle = true;
|
||
} else { // all the others get throttled
|
||
if(timer) clearTimeout(timer); // cancel #2
|
||
timer = setTimeout(() => {
|
||
fn.apply(this, args);
|
||
timer = throttle = false;
|
||
}, ms);
|
||
}
|
||
};
|
||
}
|
||
// Attach click handler to the DT
|
||
const annoteDls = window.document.querySelectorAll('dt[data-target-cell]');
|
||
for (const annoteDlNode of annoteDls) {
|
||
annoteDlNode.addEventListener('click', (event) => {
|
||
const clickedEl = event.target;
|
||
if (clickedEl !== selectedAnnoteEl) {
|
||
unselectCodeLines();
|
||
const activeEl = window.document.querySelector('dt[data-target-cell].code-annotation-active');
|
||
if (activeEl) {
|
||
activeEl.classList.remove('code-annotation-active');
|
||
}
|
||
selectCodeLines(clickedEl);
|
||
clickedEl.classList.add('code-annotation-active');
|
||
} else {
|
||
// Unselect the line
|
||
unselectCodeLines();
|
||
clickedEl.classList.remove('code-annotation-active');
|
||
}
|
||
});
|
||
}
|
||
const findCites = (el) => {
|
||
const parentEl = el.parentElement;
|
||
if (parentEl) {
|
||
const cites = parentEl.dataset.cites;
|
||
if (cites) {
|
||
return {
|
||
el,
|
||
cites: cites.split(' ')
|
||
};
|
||
} else {
|
||
return findCites(el.parentElement)
|
||
}
|
||
} else {
|
||
return undefined;
|
||
}
|
||
};
|
||
var bibliorefs = window.document.querySelectorAll('a[role="doc-biblioref"]');
|
||
for (var i=0; i<bibliorefs.length; i++) {
|
||
const ref = bibliorefs[i];
|
||
const citeInfo = findCites(ref);
|
||
if (citeInfo) {
|
||
tippyHover(citeInfo.el, function() {
|
||
var popup = window.document.createElement('div');
|
||
citeInfo.cites.forEach(function(cite) {
|
||
var citeDiv = window.document.createElement('div');
|
||
citeDiv.classList.add('hanging-indent');
|
||
citeDiv.classList.add('csl-entry');
|
||
var biblioDiv = window.document.getElementById('ref-' + cite);
|
||
if (biblioDiv) {
|
||
citeDiv.innerHTML = biblioDiv.innerHTML;
|
||
}
|
||
popup.appendChild(citeDiv);
|
||
});
|
||
return popup.innerHTML;
|
||
});
|
||
}
|
||
}
|
||
});
|
||
</script>
|
||
</div> <!-- /content -->
|
||
|
||
|
||
|
||
|
||
</body></html> |