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Jan 6, '20January 6, 2020
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Jan 7, '20January 7, 2020
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Jan 8, '20January 8, 2020
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Jan 9, '20January 9, 2020●(1 event)
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Jan 10, '20January 10, 2020
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Jan 11, '20January 11, 2020
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Jan 12, '20January 12, 2020
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| MMonday | TTuesday | WWednesday | TThursday | FFriday | SSaturday | SSunday |
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30December 30, 2019
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31December 31, 2019
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January1January 1, 2020 |
2January 2, 2020
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3January 3, 2020
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4January 4, 2020
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5January 5, 2020
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6January 6, 2020
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7January 7, 2020
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8January 8, 2020
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Keynote: Radu Horaud – |
10January 10, 2020
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11January 11, 2020
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12January 12, 2020
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13January 13, 2020
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14January 14, 2020
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15January 15, 2020
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Capacity of a LoRaWAN cell, by Martin Heusse (Drakkar) – We propose a model to estimate the packet delivery rate in a LoRaWAN cell, when all nodes have the same traffic generation process and may use repetitions. The model predicts the transmission success rate for any cell range and node density, with similar traffic from all nodes. We find that the transmission success depends on striking a balance between the adverse effects of attenuation and collisions; in small cells, it is highly dependent on the suitable allocation of spreading factors, whereas using packet repetitions is more effective in large cells. Bâtiment IMAG (442) |
17January 17, 2020
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18January 18, 2020
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19January 19, 2020
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20January 20, 2020
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21January 21, 2020
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22January 22, 2020
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Can random matrices change the future of machine learning? by Romain Couillet (Gipsa) – Romain COUILLET (professor at CentraleSupélec, University ParisSaclay; IDEX GSTATS Chair & MIAI LargeDATA Chair, University Grenoble-Alpes) |
24January 24, 2020
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25January 25, 2020
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26January 26, 2020
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27January 27, 2020
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28January 28, 2020
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29January 29, 2020
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HDR Nicolas Gast – Abstract: The design of efficient algorithms is closely linked to the evaluation of their performances. My work focuses on the use of stochastic models for the performance evaluation of large distributed systems. I am interested in developing tools that can characterize the emergent behavior of such systems and improve their performance. This leads me to solve stochastic control and optimization problems, notably through operations research methods. These problems suffer from combinatorial explosion: the complexity of a problem grows exponentially with the number of objects that compose it. It is therefore necessary to design models but also algorithmic processes whose complexity does not increase too rapidly with the size of the system.In this presentation, I will summarize a few of my contributions on the use and the refinements of mean field approxiamtion to study the performance of distributed systems and algorithms. I will introduce the key concepts behind mean field approximation, by giving some examples of where it can be applied. I will review some of the classical models and try to answer a very natural question: how large should a system be for mean field to apply? Bâtiment IMAG (amphitheater) Saint-Martin-d'Hères, 38400 France |
31January 31, 2020
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February1February 1, 2020 |
2February 2, 2020
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3February 3, 2020
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4February 4, 2020
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5February 5, 2020
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keynote LIG – |
7February 7, 2020
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8February 8, 2020
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9February 9, 2020
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10February 10, 2020
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Distributed Variational Representation Learning, by Abdellatif Zaidi (Huawei) – We connect the information flow in a neural network to sufficient statistics; and show how techniques that are rooted in information theory, such as the source-coding based information bottleneck method can lead to improved architectures, as well as a better understanding of the theoretical foundation of neural networks, viewed as a cascade compression network. We discuss distributed architectures and illustrate our results and view through some numerical examples. 442 |
12February 12, 2020
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Seminar Francois Durand: "Analysis of Approval Voting in Poisson Games" – Titre :
Analysis of Approval Voting in Poisson Games
Résumé :
Poisson games were introduced by Roger Myerson to model large elections. In this framework, the number of players of each type follows a Poisson distribution. Typically, the expected number of players is considered large, and players base their strategic response on rare events, such as a "pivot" where two candidates are tied for victory, and where one vote can make the difference. The main part of the talk will be dedicated to presenting Poisson games and the main theoretical results about them. Then I will say a few words about our work on a voting rule called Approval voting. References: Python implementation: https://poisson-approval.readthedocs.io/. |
14February 14, 2020
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15February 15, 2020
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16February 16, 2020
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Immanuel Bomze (univ. Vienna) – Robust clustering in social networks (joint with M. Kahr and M. Leitner) During the last decades the importance of considering data uncertainty in optimization problems has become increasingly apparent, since small Optimization problems where only the objective is uncertain arise, for instance, prominently in the analysis of social networks. Hence we investigate data uncertainty in the objective function of StQPs, considering different uncertainty sets, and derive implications for the complexity of robust variants of the corresponding deterministic counterparts. We can show that considering data uncertainty in a StQP results in another StQP of the same complexity if ellipsoidal, spherical or boxed uncertainty sets are assumed [4]. Moreover we discuss implications when considering polyhedral uncertainty sets, and derive rigorous bounds for this case, based upon copositive optimization [3]. References [1] Ben-Tal A, El Ghaoui L, Nemirovski AS (2009) Robust optimization. Princeton Series in Applied Mathematics (Princeton NJ: Princeton University Press). [2] Bomze IM (1998) On standard quadratic optimization problems. Journal of Global Optimization 13(4):369–387. [3] Bomze IM (2012) Copositive optimization – Recent developments and applications. European Journal of Operational Research 216(3):509–520. [4] Bomze IM, Kahr M, Leitner M. (2020) Trust your data or not - StQP remains StQP: Community Detection via Robust Standard Quadratic Optimization. To appear in Mathematics of OR. [5] Pavan M, Pelillo M (2007) Dominant sets and pairwise clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(1):167–172. [6] Rota Bulò S, Pelillo M (2017) Dominant-set clustering: A review. European Journal of Operational Research 262(1):1–13. [7] Rota Bul\`{o} S, Pelillo M, Bomze IM (2011) Graph-based quadratic optimization: A fast evolutionary approach. Computer Vision and Image Understanding 115(7):984–995. Bâtiment IMAG (442) |
18February 18, 2020
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19February 19, 2020
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Seminar Abhijnan Chakraborty – |
21February 21, 2020
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22February 22, 2020
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23February 23, 2020
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24February 24, 2020
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25February 25, 2020
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26February 26, 2020
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Strategic information transmission with receiver's type-dependent decision sets, by Stephan Sémirat (GAEL, Grenoble) – Strategic information transmission with receiver's type-dependent decision sets. Abstract: We consider a sender-receiver game, in which the sender has finitely many types and the receiver makes decisions in a bounded real interval. We assume that utility functions are concave, single-peaked and supermodular. After the cheap talk phase, the receiver makes a decision, which must fulfill a constraint (e.g., a participation constraint) that depends on the sender's type. Hence a necessary equilibrium condition is that the receiver maximizes his expected utility subject to the constraints of all positive probability types. This necessary condition may not hold at the receiver's prior belief, so that a non-revealing equilibrium may fail to exist. We propose a constructive algorithm that always achieves a partitional perfect Bayesian equilibrium Bâtiment IMAG (442) |
28February 28, 2020
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29February 29, 2020
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March1March 1, 2020 |
2March 2, 2020
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3March 3, 2020
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4March 4, 2020
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keynote LIG – |
6March 6, 2020
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7March 7, 2020
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8March 8, 2020
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