##Statistics Seminar##\\ Department of Mathematical Sciences ^ **DATE:**|Thursday, April 28, 2022 | ^ **TIME:**|1:15pm -- 2:15pm | ^ **LOCATION:**|Zoom meeting | ^ **SPEAKER:**|Baozhen Wang, Binghamton University | ^ **TITLE:**|A theory of learning from different domains | \\ **Abstract** Discriminative learning methods for classification perform well when training and test data are drawn from the same distribution. Often, however, we have plentiful labeled training data from a source domain but wish to learn a classifier which performs well on a target domain with a different distribution and little or no labeled training data. The authors investigate two questions. First, under what conditions can a classifier trained from source data be expected to perform well on target data? Second, given a small amount of labeled target data, how should we combine it during training with the large amount of labeled source data to achieve the lowest target error at test time?