MonMonday  TueTuesday  WedWednesday  ThuThursday  FriFriday  SatSaturday  SunSunday 

January 27, 2020  January 28, 2020  January 29, 2020  January 30, 2020HDR Nicolas GastHDR Nicolas GastJanuary 30, 2020 – Bâtiment IMAG (amphitheater) SaintMartind'Hères, 38400 France Abstract:

January 31, 2020  February 1, 2020  February 2, 2020 
February 3, 2020  February 4, 2020  February 5, 2020  February 6, 2020  February 7, 2020  February 8, 2020  February 9, 2020 
February 10, 2020  February 11, 2020Distributed Variational Representation Learning, by Abdellatif Zaidi (Huawei)Distributed Variational Representation Learning, by Abdellatif Zaidi (Huawei)February 11, 2020 – 442 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 sourcecoding 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. 
February 12, 2020  February 13, 2020Seminar Francois Durand: "Analysis of Approval Voting in Poisson Games"Seminar Francois Durand: "Analysis of Approval Voting in Poisson Games"February 13, 2020 – 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://poissonapproval.readthedocs.io/. 
February 14, 2020  February 15, 2020  February 16, 2020 
February 17, 2020Immanuel Bomze (univ. Vienna)Immanuel Bomze (univ. Vienna)February 17, 2020 – Bâtiment IMAG (406) SaintMartind'Hères, 38400 France 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] BenTal 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) Dominantset clustering: A review. European Journal of Operational Research 262(1):1–13. [7] Rota Bul\`{o} S, Pelillo M, Bomze IM (2011) Graphbased quadratic optimization: A fast evolutionary approach. Computer Vision and Image Understanding 115(7):984–995. 
February 18, 2020  February 19, 2020  February 20, 2020  February 21, 2020  February 22, 2020  February 23, 2020 
February 24, 2020  February 25, 2020  February 26, 2020  February 27, 2020Strategic information transmission with receiver's typedependent decision sets, by Stephan Sémirat (GAEL, Grenoble)Strategic information transmission with receiver's typedependent decision sets, by Stephan Sémirat (GAEL, Grenoble)February 27, 2020 – Bâtiment IMAG (406) SaintMartind'Hères, 38400 France Strategic information transmission with receiver's typedependent decision sets. Abstract: We consider a senderreceiver 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, singlepeaked 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 nonrevealing equilibrium may fail to exist. We propose a constructive algorithm that always achieves a partitional perfect Bayesian equilibrium 
February 28, 2020  February 29, 2020  March 1, 2020 
 June 18, 2020 @  Giorgio Fabbri (GAEL)
TBA
 June 25, 2020 @  PhD defense Dong Quan Vu
 July 2, 2020 @  Seminar: Salah Zrigui
Title: Classification of energy profiles of HPC tasks
Abstract: Salah will present how he analyzed, characterized and tried to classify energy consumption traces (time series) of HPC codes running on one of the GRICAD cluster.