A literature survey on domain adaptation of statistical classifiers, 2008. ,
A literature review of domain adaptation with unlabeled data, 2011. ,
Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.5, pp.770-787, 2010. ,
DOI : 10.1109/TPAMI.2009.57
Co-training for domain adaptation, NIPS, pp.2456-2464, 2011. ,
Correcting sample selection bias by unlabeled data, NIPS, pp.601-608, 2006. ,
Direct importance estimation with model selection and its application to covariate shift adaptation, NIPS, 2007. ,
Learning bounds for importance weighting, NIPS, pp.442-450, 2010. ,
Domain adaptation and sample bias correction theory and algorithm for regression, Theoretical Computer Science, vol.519, pp.103-126, 2014. ,
DOI : 10.1016/j.tcs.2013.09.027
A PAC-Bayesian approach for domain adaptation with specialization to linear classifiers, ICML, pp.738-746, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00822685
A bayesian divergence prior for classiffier adaptation, AISTATS, pp.275-282, 2007. ,
Analysis of representations for domain adaptation, NIPS, pp.137-144, 2006. ,
A theory of learning from different domains, Machine Learning, vol.60, issue.1-2, pp.151-175, 2010. ,
DOI : 10.1007/s10994-009-5152-4
Domain adaptation: Learning bounds and algorithms, COLT, 2009. ,
Generalization bounds for domain adaptation, NIPS, pp.3320-3328, 2012. ,
Some PAC-Bayesian theorems, Proceedings of the eleventh annual conference on Computational learning theory , COLT' 98, pp.355-363, 1999. ,
DOI : 10.1145/279943.279989
PAC-Bayes & margins, NIPS, pp.439-446, 2002. ,
PAC-Bayesian supervised classification: the thermodynamics of statistical learning, Inst. of Mathematical Statistic, vol.56, 2007. ,
URL : https://hal.archives-ouvertes.fr/hal-00206119
PAC-Bayes bounds for the risk of the majority vote and the variance of the Gibbs classifier, NIPS, pp.769-776, 2006. ,
Risk bounds for the majority vote: From a PAC-Bayesian analysis to a learning algorithm, JMLR, vol.16, pp.787-860, 2015. ,
A new PAC-Bayesian perspective on domain adaptation ,
URL : https://hal.archives-ouvertes.fr/hal-01307045
Domain adaptation with structural correspondence learning, Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, EMNLP '06, pp.120-128, 2006. ,
DOI : 10.3115/1610075.1610094
Cross Validation Framework to Choose amongst Models and Datasets for Transfer Learning, ECML-PKDD, pp.547-562, 2010. ,
DOI : 10.1007/978-3-642-15939-8_35