##Statistics Seminar##\\ Department of Mathematical Sciences ^ **DATE:**|Thursday, April 22, 2021 | ^ **TIME:**|1:15pm -- 2:15pm | ^ **LOCATION:**|Zoom meeting | ^ **SPEAKER:**|Baozhen Wang, Binghamton University | ^ **TITLE:**|A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptation | \\ **Abstract** In the problem of domain adaptation for binary classification, the learner is presented with labeled examples from a source domain, and must correctly classify unlabeled examples from a target domain, which may differ from the source. The author study a class of domain adaptation problems that generalizes both the covariate shift assumption and a model for feature-dependent label noise, and establish optimal classification on the target domain despite not having access to labelled data from this domain.