August 2019
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July 29, 2019 July 30, 2019 July 31, 2019 August 1, 2019 August 2, 2019 August 3, 2019 August 4, 2019
August 5, 2019 August 6, 2019 August 7, 2019 August 8, 2019 August 9, 2019 August 10, 2019 August 11, 2019
August 12, 2019 August 13, 2019 August 14, 2019 August 15, 2019 August 16, 2019 August 17, 2019 August 18, 2019
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August 26, 2019 August 27, 2019 August 28, 2019 August 29, 2019 August 30, 2019 August 31, 2019 September 1, 2019
  • June 13, 2019 @ Bâtiment IMAG (406) -- How much Quantum information can improve Social Welfare, by Mehdi Mhalla (LIG)

    How much Quantum information can improve Social Welfare.

    It has been shown in [1] that quantum resources can allow to achieve a family of Nash equilibria that can have sometimes a better social welfare while guaranteeing privacy.
    We use graph games to propose a way to build non cooperative games from graph states and show how we can achieve an unlimited improvement with the quantum advices compared to the classical one. We also discuss the notion of guaranteed utility that these games provide.

    This is a work in progress with Berry Groisman Michael Mc Gettrick and Marcin Pawlowski.

    [1] Vincenzo Auletta, Diodato Ferraioli, Ashutosh Rai, Giannicola Scarpa, and Andreas Winter. Belief-invariant and quantum equilibria in games of incomplete information. arXiv preprint arXiv:1605.07896, 2016.

  • June 20, 2019 @ Bâtiment IMAG (406) -- If Agents Could Talk… What Should They Say? by Jason Marden (Univ of California, Santa Barbara)

    Title: If Agents Could Talk… What Should They Say?

    Abstract: The goal in networked control of multiagent systems is to derive desirable collective behavior through the design of local control algorithms. The information available to the individual agents, either through sensing or communication, invariably defines the space of admissible control laws. Hence, informational restrictions impose constraints on the achievable performance guarantees. The first part of this talk will provide one such constraint with regards to the efficiency of the resulting stable solutions for a class of distributed submodular optimization problems. Further, we will also discuss how strategic information exchange can help mitigate these degradations. The second part of this talk will focus on how agents should utilize available information to optimize the efficiency of the emergent collective behavior. In particular, we will discuss a methodology for optimizing the efficiency guarantees (i.e., price of anarchy) in distributed resource allocation problems through the design of local agent objective functions. Lastly, we will highlight some unintended consequences associated with these optimal designed agent objective functions – optimizing the performance of the worst-case equilibria (i.e., price of anarchy) often comes at the expense of the best-case equilibria (i.e., price of stability).

    Bio: Jason R. Marden is an Associate Professor in the Department of Electrical and Computer, Engineering at the University of California, Santa Barbara. Jason received a BS in Mechanical Engineering in 2001 from UCLA, and a PhD in Mechanical Engineering in 2007, also from UCLA, under the supervision of Jeff S. Shamma, where he was awarded the Outstanding Graduating PhD Student in Mechanical Engineering. After graduating from UCLA, he served as a junior fellow in the Social and Information Sciences Laboratory at the California Institute of Technology until 2010 when he joined the University of Colorado. In 2015, Jason joined the Department of Electrical and Computer Engineering at the University of California, Santa Barbara. Jason is a recipient of the ONR Young Investigator Award (2015), NSF Career Award (2014), the AFOSR Young Investigator Award (2012), the American Automatic Control Council Donald P. Eckman Award (2012), and the SIAM/SGT Best Sicon Paper Award (2015). Furthermore, Jason is also an advisor for the students selected as finalists for the best student paper award at the IEEE Conference on Decision and Control (2011, 2016, 2017). Jason's research interests focus on game theoretic methods for the control of distributed multiagent systems.

  • July 1, 2019 @ ENSIMAG - Amphi D -- Soutenance de thèse Michael Mercier : Contribution à la convergence d'infrastructure entre le calcul haute performance (HPC) et le traitement de données a large échelle (Big Data)

    La quantité de données produites, dans le monde scientifique comme dans le monde commercial, est en constante augmentation. Cela implique un changement d'échelle pour les processus de collecte, de traitement, et de stockage des données. Le domaine du Big Data a été inventé pour traiter ces problèmes. Mais ce domaine a été inventé par les géants du web et son intégration sur des machine de calcul intensif pose de nombreux problèmes. En effet, les gestionnaires de ressources utilisé pour soumettre des travaux sur des super calculateurs et les gestionnaires de ressources des frameworks Big Data sont très différents et ne fonctionnent pas ensemble. De plus, les outils d'analyse pour le Big Data ne savent pas tirer partie du matériel très haute performance. Le sujet de ma thèse est de trouver la meilleur approche pour faire interagir ces deux gestionnaires de ressources et de traiter les différents problèmes soulevés par les mouvement de données et leur ordonnancements.

  • September 12, 2019 @ -- MFG or MDP by Ziliotto Bruno (CNRS, Paris)


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