seminars:stat:210422 [Department of Mathematics and Statistics, Binghamton University]

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<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematical Sciences</WRAP>

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^  **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  |
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<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.
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