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--- seminars:stat:200917 [2020/09/05 12:41] – created qyu
+++ seminars:stat:200917 [2020/09/16 12:16] (current) – qyu
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+<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematical Sciences</WRAP>
+<WRAP 70% center>
+^  **DATE:**|Thursday, Sept. 24, 2020 |
+^  **TIME:**|1:15pm -- 2:15pm |
+^  **LOCATION:**|Zoom meeting |
+^  **SPEAKER:**|Zhou Wang, Binghamton  University |
+^  **TITLE:**|Consistency of Plug-in Confidence Sets for Classification in Semi-supervised Learning|
+</WRAP>
+\\
+<WRAP center box 80%>
+<WRAP centeralign>**Abstract**</WRAP>
+Confident prediction is highly relevant in machine learning; for example, in
+applications
+such as medical diagnoses, wrong prediction can be fatal. For classification, there
+already exists procedures that allow to not classify data when the confidence in their
+prediction is weak. This approach is known as classification with reject option. In the
+this paper, the authors provide new methodology for this approach. Predicting a new
+instance via a confidence set, they ensure an exact control of the probability of
+classification. Moreover, they show that this methodology is easily implementable and
+entails attractive theoretical and numerical properties.
+</WRAP>