Domain adaptation and sample bias correction

Description

Domain adaptation is a challenging learning problem where very few or no labeled points are available from the target domain, but where the learner receives a labeled training sample from a source domain somewhat close to the target domain and where he typically can further access a large set of unlabeled points from a target domain. A related problem is that of sample bias correction.


Related Publications
Corinna Cortes and Mehryar Mohri.
Domain adaptation and sample bias correction theory and algorithm for regression.
Theoretical Computer Science, 9474, 2013.

Mehryar Mohri and Andres Muñoz Medina.
New analysis and algorithm for learning with drifting distributions.
In Proceedings of The 23rd International Conference on Algorithmic Learning Theory (ALT 2012). volume 7568, pages 124-138, Lyon, France, October 2012. Springer, Heidelberg, Germany.

Corinna Cortes and Mehryar Mohri.
Domain adaptation in regression.
In Proceedings of The 22nd International Conference on Algorithmic Learning Theory (ALT 2011). volume to appear, Espoo, Finland, October 2011. Springer, Heidelberg, Germany.

Corinna Cortes, Yishay Mansour, and Mehryar Mohri.
Learning bounds for importance weighting.
In Advances in Neural Information Processing Systems (NIPS 2010). Vancouver, Canada, 2010. MIT Press.

Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh.
Multiple source adaptation and the Rényi divergence.
In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009). Montréal, Canada, June 2009.

Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh.
Domain adaptation: Learning bounds and algorithms.
In Proceedings of The 22nd Annual Conference on Learning Theory (COLT 2009). Montréal, Canada, June 2009. Omnipress. Longer arxiv version.

Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh.
Domain adaptation with multiple sources.
In Advances in Neural Information Processing Systems (NIPS 2008). pages 1041-1048, Vancouver, Canada, 2009. MIT Press.

Corinna Cortes, Mehryar Mohri, Michael Riley, and Afshin Rostamizadeh.
Sample selection bias correction theory.
In Proceedings of The 19th International Conference on Algorithmic Learning Theory (ALT 2008). volume 5254 of Lecture Notes in Computer Science, pages 38-53, Budapest, Hungary, October 2008. Springer, Heidelberg, Germany.