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--- seminars:stat:220428 [2022/04/25 12:15] – created qyu
+++ seminars:stat:220428 [2022/04/25 12:17] (current) – qyu
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+<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematical Sciences</WRAP>
+<WRAP 70% center>
+^  **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  |
+</WRAP>
+\\
+<WRAP center box 80%>
+<WRAP centeralign>**Abstract**</WRAP>
+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?
+</WRAP>