December 1, 2022
Séminaire Stephane Durand
Séminaire Stephane Durand
–
December 1, 2022
Jeux de contagions et d'influences: les différentes formes, les approches et le contexte
-
News
- Journée au vert POLARIS 2022/05/23
- DATAMOVE/POLARIS picnic 2021/06/22
- DATAMOVE/POLARIS BBQ 2019 2019/06/14
- POLARIS Bootcamp (May 2019) 2019/05/24
- slides of Andras Gyorgy 2016/01/15
Next seminars
Events
Events in December 2022–January 2023
MMonday TTuesday WWednesday TThursday FFriday SSaturday SSunday 28November 28, 202229November 29, 202230November 30, 2022December
1December 1, 2022●(1 event)Séminaire Stephane Durand
–
December 1, 2022Jeux de contagions et d'influences: les différentes formes, les approches et le contexte
2December 2, 20223December 3, 20224December 4, 20225December 5, 20226December 6, 20227December 7, 2022Séminaire GLSI / CtrlA: Quentin Guilloteau (Datamove)
–
December 8, 2022Bâtiment IMAG (442)Seminar Mario Bravo (room 106)
–
December 8, 20229December 9, 202210December 10, 202211December 11, 2022Seminar Matthieu Jonckheere (room 306)
–
December 12, 2022Title: Parameter Selection in Fermat Distances: Navigating Geometry and Noise
Abstract: Fermat distances are metrics that can be inferred from datasets. In their empirical (microscopic) version, they are defined following the model of first passage Euclidean percolation. The macroscopic (population) version establishes a metric that depends on the density from which the points were sampled. This characteristic makes these distances useful for various tasks such as classification, clustering, topological learning, optimal transport, and Wasserstein barycenter computation,...
The distances hinge on a parameter that requires careful selection. Throughout this presentation, we will delve into how these distances can effectively determine clusters at both the population and empirical levels, supported by consistency theorems. Moreover, we will leverage this understanding to gain insights on the choice of the parameter. The exploration of the asymptotic behavior of these distances translates into first-pass percolation problems, many of which remain unsolved.13December 13, 202214December 14, 2022PhD defense Chen Yan: Poliques quasi-optimal pour les restless bandits.
–
December 15, 2022Thèse supervisée par Nicolas GAST et Bruno GAUJAL.La soutenance aura lieu le jeudi 15 décembre 2022 à 14h00 à l'amphithéâtre 1 de la Tour IRMA (51 rue des mathématiques, 38610 Gières). Un pot aura lieu après la soutenance à salle 406 du bâtiment IMAG.Jury:-- David Alan Goldberg, Professeur associé, Université de Cornell (Rapporteur)-- Bruno Scherrer, Chargé de recherche, Inria Nancy (Rapporteur)-- Jérôme Malick, Directeur de recherche, CNRS (Examinateur)-- Nguyễn Kim Thắng, Professeur, Université Grenoble Alpes (Examinateur)-- LEGROS Benjamin, Professeur associé, EM Normandie (Examinateur)Résumé :Bandits are one of the most basic examples of decision-making with uncertainty. A Markovian restless bandit can be seen as the following sequential allocation problem: At each decision epoch, one or several arms are activated (pulled); all arms generate an instantaneous reward that depend on their state and their activation; the state of each arm then changes in a Markovian fashion, based on an underlying transition matrix. Both the rewards and the probability matrices are known, and the new state is revealed to the decision maker for its next decision. The word restless serves to emphasize the fact that arms that are not activated can also change states, hence generalizes the simpler rested bandits. In principle, the above problem can be solved by dynamic programming, since it is a Markov decision process. The challenge that we face is the curse of dimension, since the size of possible states and actions grows exponentially with the number of arms of the bandit. Consequently, the focus is to design policies that solve the dilemma of computational efficiency and close-to-optimal performance.In this thesis, we construct computationally efficient policies with provable performance bounds, that may differ depending on certain properties of the problem. We first investigate the classical Whittle index policy (WIP) on infinite horizon problems, and prove that if it is asymptotically optimal under the global attractor assumption, then almost always it converges to the optimal value exponentially fast. The application of WIP has the additional technical assumption of indexability as a prerequisite, to get around this, we next study the LP-index policy, that is well-defined for any problem, and shares the same exponential speed of convergence as WIP under similar assumptions.In infinite horizon, we always need the global attractor assumption for asymptotic optimality. We next study the problem under finite horizon, so that this assumption is no-longer a concern. Instead, the LP-compatibility and the non-degeneracy are required for the asymptotic optimality and a faster convergence rate. We construct the finite horizon LP-index policy, as well as the LP-update policy, that amounts to solving new LP-index policies during the evolution of the process. This latter LP-update policy is then generalized to the broader framework of weakly coupled MDPs, together with the generalization of the non-degenerate condition. This condition also allows a more efficient implementation of the LP-update policy, as well as a faster convergence rate, if it is satisfied on the weakly coupled MDPs.Tour IRMA, campus Saint Martin d'Hères16December 16, 202217December 17, 202218December 18, 202219December 19, 202220December 20, 202221December 21, 202222December 22, 202223December 23, 202224December 24, 202225December 25, 202226December 26, 202227December 27, 202228December 28, 202229December 29, 202230December 30, 202231December 31, 2022January
1January 1, 20232January 2, 20233January 3, 20234January 4, 20235January 5, 20236January 6, 20237January 7, 20238January 8, 20239January 9, 202310January 10, 202311January 11, 202312January 12, 202313January 13, 202314January 14, 202315January 15, 202316January 16, 202317January 17, 202318January 18, 202319January 19, 202320January 20, 202321January 21, 202322January 22, 202323January 23, 202324January 24, 202325January 25, 202326January 26, 202327January 27, 202328January 28, 202329January 29, 202330January 30, 202331January 31, 2023February
1February 1, 20232February 2, 20233February 3, 20234February 4, 20235February 5, 2023Meta
