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Ph.D de

Ph.D
Group : Learning and Optimization

Elements for Learning and Optimizing Expensive Functions

Starts on 01/12/2007
Advisor : SEBAG, Michèle

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

Defended on 22/12/2010, committee :
* Jean-Yves Audibert (rapporteur), Ecole des Ponts-ParisTech;
* Guillaume Deffuant, Cemagref;
* Damien Ernst, Université de Liège (Belgique);
* Claudio Gentile (rapporteur), Universita' dell'Insubria, Varese (Italie);
* Michèle Sebag (directrice de thèse), Université Paris-Sud;
* Olivier Teytaud (directeur de thèse), Université Paris-Sud.

Research activities :

Abstract :
This work focuses on learning and optimizing expensive functions, that
is constructing algorithms learning to identify a concept, to
approximate a function or to find an optimum based on examples of this
concept (resp. points of the function).

The motivating application is learning and optimizing simplified models
in numerical engineering, for industrial challenges for which obtaining
examples is expensive. It is then necessary to use as few examples as
possible for learning (resp. optimizing).

The first contribution was the conception and development of a new
approach of active learning, based on reinforcement learning.
Theoretical foundations for this approach were established. Furthermore,
a learning algorithm based on this approach, BAAL, was implemented, and
used to provide experimental validation.

The approach, originally focused on machine learning, was also extended
to optimization.

The second contribution is focused on the potential and limits of both
active learning and expensive optimization, from a theoretical point of
view. Sample complexity bounds were derived: 1/ for batch active
learning; 2/ for noisy optimization.

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