Name: Seminar Mario Bravo (room 106)
Start: 2022-12-08T14:00:00+02:00
End: 2022-12-08T15:00:00+02:00

Calendar

The week's events

Séminaire GLSI / CtrlA: Quentin Guilloteau (Datamove)

2:00 pm – 3:00 pm
December 8, 2022

Bâtiment IMAG (442)

Saint-Martin-d'Hères, 38400

France

View Location

Map

Seminar Mario Bravo (room 106)

2:00 pm – 3:00 pm
December 8, 2022

Comments are closed.

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 in December 2022–January 2023

M	T	W	T	F	S	S
28	29	30	December 1 Séminaire Stephane Durand 2:00 pm – 3:00 pm December 1, 2022 Jeux de contagions et d'influences: les différentes formes, les approches et le contexte Read more	2	3	4
5	6	7	8 Séminaire GLSI / CtrlA: Quentin Guilloteau (Datamove) 2:00 pm – 3:00 pm December 8, 2022 Bâtiment IMAG (442) Saint-Martin-d'Hères, 38400 France View Location Map Read more, Seminar Mario Bravo (room 106) 2:00 pm – 3:00 pm December 8, 2022 Read more	9	10	11
12 Seminar Matthieu Jonckheere (room 306) 11:00 am – 12:00 pm December 12, 2022 Title: 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. Read more	13	14	15 PhD defense Chen Yan: Poliques quasi-optimal pour les restless bandits. 2:00 pm – 5:00 pm December 15, 2022 Thèse 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'Hères View Location Read more	16	17	18
19	20	21	22	23	24	25
26	27	28	29	30	31	January 1
2	3	4	5	6	7	8
9	10	11	12	13	14	15
16	17	18	19	20	21	22
23	24	25	26	27	28	29
30	31	February 1	2	3	4	5

Meta

Calendar

The week's events

Séminaire GLSI / CtrlA: Quentin Guilloteau (Datamove)

Seminar Mario Bravo (room 106)

News

Next seminars

Events

Events in December 2022–January 2023

December

January

February

Meta