<html xmlns:v="urn:schemas-microsoft-com:vml" xmlns:o="urn:schemas-microsoft-com:office:office" xmlns:w="urn:schemas-microsoft-com:office:word" xmlns:m="http://schemas.microsoft.com/office/2004/12/omml" xmlns="http://www.w3.org/TR/REC-html40"><head><META HTTP-EQUIV="Content-Type" CONTENT="text/html; charset=us-ascii"><meta name=Generator content="Microsoft Word 15 (filtered medium)"><style><!--
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</o:shapelayout></xml><![endif]--></head><body lang=EN-US link="#0563C1" vlink="#954F72"><div class=WordSection1><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>Seminar on Theoretical Machine Learning<o:p></o:p></p><p class=MsoNormal>Topic: A Theoretical Analysis of Contrastive Unsupervised Representation Learning<o:p></o:p></p><p class=MsoNormal>Speaker: Orestis Plevrakis, Princeton University<o:p></o:p></p><p class=MsoNormal>Time/Room: 1:30pm – 2:45pm/White Levy Room<o:p></o:p></p><p class=MsoNormal>Abstract Link: <a href="http://www.math.ias.edu/seminars/abstract?event=139490">http://www.math.ias.edu/seminars/abstract?event=139490</a><o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>Recent empirical works have successfully used unlabeled data to learn feature representations that are broadly useful in downstream classification tasks. Several of these methods are reminiscent of the well-known word2vec embedding algorithm: leveraging availability of pairs of semantically “similar” data points and “negative samples,” the learner forces the inner product of representations of similar pairs with each other to be higher on average than with negative samples. The current paper uses the term contrastive learning for such algorithms and presents a theoretical framework for analyzing them by introducing latent classes and hypothesizing that semantically similar points are sampled from the same latent class. This framework allows us to show provable guarantees on the performance of the learned representations on the average classification task that is comprised of a subset of the same set of latent classes. Our generalization bound also shows that learned representations can reduce the (labeled) sample complexity on downstream tasks. We conduct controlled experiments in both the text and image domains to support the theory.<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p></div></body></html>