Symposium on Machine Learning in Speech and Language Processing (MLSLP)
September 14, 2012
Portland, Oregon, USA
Speaker: Shai Ben-David (University of Waterloo)
Title: Theoretical Analysis of Domain Adaptation -- Current State of the Art
Abstract:
Domain adaptation (DA) learning occurs often in practice. It refers to
situations in which the training data available to the learner is
(generated by a distribution that is) different than (the distribution
generating) the target task that the learnt predictor will be
evaluated on.
Theoretical work on domain adaptation aims to characterize situations
in which such learning is possible and to propose appropriate learning
paradigms and algorithms.
In this talk I will survey the current theoretical understanding of DA
learning and list some directions in which further research may
require collaboration with people that encounter such learning issues
in practice.