Information détaillée concernant le cours
Titre | École d’été 2024 |
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Dates | 1-4 septembre 2024 |
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Responsable de l'activité | Christian Mazza |
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Organisateur(s)/trice(s) | Prof. Christian Mazza, Université de Fribourg |
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Intervenant-e-s | Prof. Bartek Blaszczyszyn, INRIA, Paris, France |
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Description | Prof. Bartek Blaszczyszyn, INRIA, Paris, France Prof. Roland Langrock, Bielefeld University, Allemagne Title : Hidden Markov models Prof. Antonietta Mira, Università della Svizzera italiana, Suisse Title : How can Bayesian statistics help in dimensionality reduction? Abstract : I will introduce the Bayesian paradigm to statistical inference, and then explain how it can be exploited to estimate the intrinsic dimension (ID) of data, and to answer questions related to dimensionality reduction that are becoming more pressing as the size of available data becomes larger. Indeed, real-world datasets tend to show a high degree of (possibly) non-linear correlations and constraints between their features. This means that, despite a very large embedding dimensionality, data typically lie on a manifold characterized by a much lower ID. which, in the presence of noise, may depend on the scale at which the data is analysed. This fact rises interesting questions: How many variables, or combinations there of, are necessary to describe a real-world data set without significant information loss? What is the appropriate scale at which one should analyze and visualize the data? These two issues, which are often considered unrelated, are actually strongly entangled, and can be addressed within a unified framework. We introduce an approach in which the optimal number of variables and the optimal scale are determined self-consistently, recognizing and bypassing the scale at which the data are affected by noise. To this aim we estimate the data ID in an adaptive way, and exploit it as a summary statistics in Approximate Bayesian Computation for inference in network type data. Sometimes, within the same dataset, it is possible to identify more than one ID meaning that different subsets of the data points lie onto manifolds with different IDs. Identifying these manifold provides a clustering of the data, and in many real world applications a simple topological feature, like the ID, allows to uncover a rich data structure and improves our insight into subsequent statistical analysis. Examples of these applications range from gene expression to protein folding, pandemic evolution, FMRI, all the way to finance, sport data and the analysis of the representations of neural networks. |
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Programme |
Program (can be changed)
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Lieu |
Anzère - Eden Resort - Alpes valaisannes |
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Information | Hôtel : Eden Resort Anzère Adress : Route d'Anzère 34, 1972 Anzère, Canton du Valais Tel +41 (0)27 399 31 00 En transport public De: Aéroport de Genève : Train jusqu'à Sion. A la gare de Sion : car postal Sion - Anzère centre (36 mns) Swiss Train schedule : From: Geneva airport, To: Sion. From Sion station : postal bus to Anzère centre. Travel time: Genève - Sion (1 hour and 55 minutes); Sion - Anzère (36 minutes).
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Frais | Tarif : CUSO (UNINE, UNIGE, UNIL, UNIFR, UNIBE, IHEID, HES-SO) Doctorant-e CUSO chambre double: 200 CHF Doctorant-e CUSO chambre simple: 350 CHF Post-doctorant-e CUSO chambre double: 300 CHF Post-doctorant-e CUSO chambre simple: 450 CHF Professeur-e CUSO chambre double: 400 CHF Professeur-e CUSO chambre simple: 550 CHF Non CUSO privé-e chambre double: 1300 CHF Non CUSO privé-e chambre simple: 1500 CHF Lors de votre inscription, merci de bien vouloir indiquer dans la zone commentaire si vous désirez une chambre simple, ou double et le nom de la personne avec qui vous souhaiteriez partager votre chambre. Dans le cas où rien n'est indiqué, une chambre simple sera réservée. Si vous vous inscrivez et que vous ne pouvez pas participer, veuillez nous contacter dès que possible. Dans le cas contraire, toutes les nuits d'hôtel pourraient vous être facturées.
*Condition d'annulation : Pour une annulation effectuée entre la date de réservation et 30 jours avant l'arrivée aucun frais d'annulation ne sera prélevé.Pour une annulation effectuée entre 7 et 30 jours avant l'arrivée un montant équivalent à 30 % de la réservation sera prélevé.Pour une annulation effectuée entre 7 jours avant l'arrivée et le jour de l'arrivée la Totalité de la réservation sera prélevé.
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Inscription | Versement sur compte postal (payment into postal account) : CUSO
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Places | 30 |
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Délai d'inscription | 24.08.2024 |
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