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Machine Learning-Robotics

Domaine
Machine Learning-Robotics
Domain - extra
Année
2014
Starting
01/10/2014
État
Open
Sujet
An empirical approach to machine learning: algorithm selection, hyperparameter optimization, and automatic principle design
Thesis advisor
KÉGL Balázs
Co-advisors
Michele Sebag
Laboratory
EXT
Collaborations
Abstract
In this thesis project we propose to apply the scientific method to machine learning. We will explore two lines of research. In the first we will build on recent work applying modern experimental design for algorithm selection and hyperparameter tuning. The main thrust of this sub-project is the multi-problem approach: we will explore the interaction between methods (and hyperparameters) and data sets to find out whether and to what extent experience can be generalized across data sets. The output of this project is a toolbox for practitioners and a stockpile of knowledge on what algorithm works on what (kind of) data sets. This second output will feed into the second line of research: we will ask the question of \emph{why} certain methods work on certain data sets. We will study algorithms as natural phenomena, form hypotheses, design and evaluate experiments, and carry out measurements that could validate or refute our hypotheses.
Context
Objectives
Work program
Extra information
Prerequisite
Détails
Télécharger empirical.pdf
Expected funding
Institutional funding
Status of funding
Expected
Candidates
Sourava Prasad Mishra
Utilisateur
balazs.kegl
Créé
Dimanche 18 mai 2014 08:49:34 CEST
dernière modif.
Dimanche 18 mai 2014 08:57:10 CEST

Fichiers joints

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empirical.pdf 18 mai 2014 08:57331188.42 Kb


Ecole Doctorale Informatique Paris-Sud


Directrice
Nicole Bidoit
Assistante
Stéphanie Druetta
Conseiller aux thèses
Dominique Gouyou-Beauchamps

ED 427 - Université Paris-Sud
UFR Sciences Orsay
Bat 650 - aile nord - 417
Tel : 01 69 15 63 19
Fax : 01 69 15 63 87
courriel: ed-info à lri.fr