NIPS 2011 Domain Adaptation Workshop: On the utility of unlabeled samples in Domain Adaptation

Domain Adaptation Workshop: Theory and Application at NIPS 2011 Invited Speaker: On the utility of unlabeled samples in Domain Adaptation by Shai Ben-David Shai Ben-David grew up in Jerusalem, Israel and attended the Hebrew University studying physics, mathematics and psychology. He received his PhD under the supervision of Saharon Shelah and Menachem Magidor for a thesis in set theory (on non-provability of infinite combinatorial statements). In August 2004 he joined the School of Computer Science at the University of Waterloo. Abstract: In many domain adaptation applications, on top of a sample of labeled points generated by the training tasks, the learner can also access unlabeled samples generated by the target distribution. The focus of this talk is to investigate when can such unlabeled samples be (provably) beneficial to the leaner. We show that depending on the type of prior knowledge available to the leaner, there are setups in which unlabeled target-generated samples can make a big difference in the required size of labeled training samples, while in other scenarios such unlabeled samples do not improve the learning rate.