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Ph.D de

Ph.D
Group : Learning and Optimization

Portfolio methods in uncertain contexts

Starts on 14/03/2013
Advisor : TEYTAUD, Olivier

Funding :
Affiliation : Université Paris-Sud
Laboratory : LRI-TAO

Defended on 11/12/2015, committee :
Directeurs de thèse :
M. Olivier Teytaud, INRIA Saclay
M. Marc Schoenauer, INRIA Saclay

Rapporteurs :
M. Bruno Bouzy, Université Paris Descartes

Examinateurs :
M. Philippe Dague, Université Paris-Saclay
M. Simon Lucas, University of Essex
M. Petr Posik, Gerstner Laboratory
M. Günter Rudolph, University of Dortmund

Research activities :

Abstract :
The energy investments are difficult because of uncertainties. Some uncertainties can be modeled by the probabilities. But there are difficult issues such as the evolution of technology and the penalization of CO2, which can not be presented by probabilities. Also, in the traditional optimization of energy systems, disappointingly, the noise is often badly treated by deterministic management. This thesis focuses on applying noisy optimization to energy systems. This thesis concentrates in studying methods to handle noise, including using of resampling methods to improve the convergence rates; applying portfolio methods to noisy optimization in the continuous domain; applying portfolio methods to the energy investments and games, including the use of adversarial bandit algorithms to calculate the Nash equilibrium of two-player zero-sum matrix game and the use of "sparsity" to accelerate the computation of Nash equilibrium.

Ph.D. dissertations & Faculty habilitations
DECODING THE PLATFORM SOCIETY: ORGANIZATIONS, MARKETS AND NETWORKS IN THE DIGITAL ECONOMY
The original manuscript conceptualizes the recent rise of digital platforms along three main dimensions: their nature of coordination devices fueled by data, the ensuing transformations of labor, and the accompanying promises of societal innovation. The overall ambition is to unpack the coordination role of the platform and where it stands in the horizon of the classical firm – market duality. It is also to precisely understand how it uses data to do so, where it drives labor, and how it accommodates socially innovative projects. I extend this analysis to show continuity between today’s society dominated by platforms and the “organizational society”, claiming that platforms are organized structures that distribute resources, produce asymmetries of wealth and power, and push social innovation to the periphery of the system. I discuss the policy implications of these tendencies and propose avenues for follow-up research.

DISTRIBUTED COMPUTING WITH LIMITED RESOURCES


VALORISATION DES DONNéES POUR LA RECHERCHE D'EMPLO