Date/heure
Date(s) - 16/12/2013
10 h 55 min - 11 h 55 min

Catégories Pas de Catégories

$Domain Adaptation of Weighted Majority Votes via Perturbed Variation-based Self-Labeling By Emilie Morvant, IST Austria. Talk at Friiam/Frumam, 2e étage, St Charles In machine learning, the domain adaptation problem arrives when the test (target) and the train (source) data are generated from different distributions. A key applied issue is thus the design of algorithms able to gen- eralize on a new distribution, for which we have no label information. We consider the specific PAC-Bayesian situation focused on learning classification models defined as a weighted majority vote over a set of real- valued functions. In this context, we present PV-MinCq a new framework that general- izes a non-adaptative algorithm (MinCq). PV-MinCq follows the next principle. Jus- tified by a theoretical bound on the tar- get risk of the vote, we provide to MinCq a target sample labeled thanks to a per- tubed variation-based self-labeling focalized on the regions where the source and target marginals appear similar. We also study the influence of our self-labeling, from which we deduce an original process for tuning the hy- perparameters. Our experiments show very promising results on a synthetic problem.$

Date/heure
Date(s) - 16/12/2013
10 h 55 min - 11 h 55 min

Catégories Pas de Catégories

$Domain Adaptation of Weighted Majority Votes via Perturbed Variation-based Self-Labeling\n\nBy Emilie Morvant, IST Austria.\n\nTalk at Friiam/Frumam, 2e étage, St Charles\n\nIn machine learning, the domain adaptation problem arrives when the test (target) and the train (source) data are generated from different distributions. A key applied issue is thus the design of algorithms able to gen- eralize on a new distribution, for which we have no label information. We consider the specific PAC-Bayesian situation focused on learning classification models defined as a weighted majority vote over a set of real- valued functions. In this context, we present PV-MinCq a new framework that general- izes a non-adaptative algorithm (MinCq). PV-MinCq follows the next principle. Jus- tified by a theoretical bound on the tar- get risk of the vote, we provide to MinCq a target sample labeled thanks to a per- tubed variation-based self-labeling focalized on the regions where the source and target marginals appear similar. We also study the influence of our self-labeling, from which we deduce an original process for tuning the hy- perparameters. Our experiments show very promising results on a synthetic problem.[$

Date/heure
Date(s) - 16/12/2013
10 h 55 min - 11 h 55 min

Catégories Pas de Catégories

$Domain Adaptation of Weighted Majority Votes via Perturbed Variation-based Self-Labeling\n\nBy Emilie Morvant, IST Austria.\n\nTalk at Friiam/Frumam, 2e étage, St Charles\n\nIn machine learning, the domain adaptation problem arrives when the test (target) and the train (source) data are generated from different distributions. A key applied issue is thus the design of algorithms able to gen- eralize on a new distribution, for which we have no label information. We consider the specific PAC-Bayesian situation focused on learning classification models defined as a weighted majority vote over a set of real- valued functions. In this context, we present PV-MinCq a new framework that general- izes a non-adaptative algorithm (MinCq). PV-MinCq follows the next principle. Jus- tified by a theoretical bound on the tar- get risk of the vote, we provide to MinCq a target sample labeled thanks to a per- tubed variation-based self-labeling focalized on the regions where the source and target marginals appear similar. We also study the influence of our self-labeling, from which we deduce an original process for tuning the hy- perparameters. Our experiments show very promising results on a synthetic problem.[$