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--- seminars:stat:210422 [2021/04/13 16:23] – created qyu
+++ seminars:stat:210422 [2021/04/13 16:24] (current) – qyu
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+<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematical Sciences</WRAP>
+<WRAP 70% center>
+^  **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  |
+</WRAP>
+\\
+<WRAP center box 80%>
+<WRAP centeralign>**Abstract**</WRAP>
+In the problem of domain adaptation for binary
+classi&#64257;cation, 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 classi&#64257;cation on the target domain despite not having
+access to labelled data from this domain.
+</WRAP>