A review of domain adaptation without target labels

Domain adaptation has become a prominent problem setting in machine learning and related fields. This review asks the question: how can a classifier learn from a source domain and generalize to a target domain? We present a categorization of approaches, divided into, what we refer to as, sample-based, feature-based and inference-based methods. Sample-based methods focus on weighting individual observations during training based on their importance to the target domain. Feature-based methods revolve around on mapping, projecting and representing features such that a source classifier performs well on the target domain and inference-based methods incorporate adaptation into the parameter estimation procedure, for instance through constraints on the optimization procedure. Additionally, we review a number of conditions that allow for formulating bounds on the cross-domain generalization error. Our categorization highlights recurring ideas and raises questions important to further research.Comment: 20 pages, 5 figure

Bornes PAC-Bayes et algorithmes d'apprentissage

Tableau d’honneur de la Faculté des études supérieures et postdoctorales, 2010-2011L’objet principale de cette thèse est l’étude théorique et la conception d’algorithmes d’apprentissage concevant des classificateurs par vote de majorité. En particulier, nous présentons un théorème PAC-Bayes s’appliquant pour borner, entre autres, la variance de la perte de Gibbs (en plus de son espérance). Nous déduisons de ce théorème une borne du risque du vote de majorité plus serrée que la fameuse borne basée sur le risque de Gibbs. Nous présentons également un théorème permettant de borner le risque associé à des fonctions de perte générale. À partir de ce théorème, nous concevons des algorithmes d’apprentissage construisant des classificateurs par vote de majorité pondérés par une distribution minimisant une borne sur les risques associés aux fonctions de perte linéaire, quadratique, exponentielle, ainsi qu’à la fonction de perte du classificateur de Gibbs à piges multiples. Certains de ces algorithmes se comparent favorablement avec AdaBoost.The main purpose of this thesis is the theoretical study and the design of learning algorithms returning majority-vote classifiers. In particular, we present a PAC-Bayes theorem allowing us to bound the variance of the Gibbs’ loss (not only its expectation). We deduce from this theorem a bound on the risk of a majority vote tighter than the famous bound based on the Gibbs’ risk. We also present a theorem that allows to bound the risk associated with general loss functions. From this theorem, we design learning algorithms building weighted majority vote classifiers minimizing a bound on the risk associated with the following loss functions : linear, quadratic and exponential. Also, we present algorithms based on the randomized majority vote. Some of these algorithms compare favorably with AdaBoost