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<div class="PlainText">This is an announcement of Jafar Jafarov's Dissertation Defense.<br>
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Candidate: Jafar Jafarov<br>
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Date: Wednesday, June 08, 2022<br>
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Time: 12 pm CST<br>
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Remote Location: <a href="https://uchicago.zoom.us/j/93270019541?pwd=SVJwVkcwZHdpb1hOQml6WVYzYTNxZz09">
https://uchicago.zoom.us/j/93270019541?pwd=SVJwVkcwZHdpb1hOQml6WVYzYTNxZz09</a> Meeting ID: 932 7001 9541 Passcode: 305048<br>
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Location: JCL 298<br>
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Title: Correlation Clustering with Local and Global Objectives<br>
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Abstract: In the Correlation Clustering problem, we are given a graph with its edges labeled as "similar"; and "dissimilar" by a noisy binary classifier, and the goal is to produce a clustering of the vertex set which matches with the edge labels as much as
possible. Correlation Clustering has been mainly studied under two models where the input graph is (i) complete and unweighted, and (ii) arbitrary and weighted.<br>
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In this thesis, we introduce a new model of Correlation Clustering that better captures real life instances. In this model the input graph is complete with bounded edge weights. We give an approximation algorithm and give a matching integrality gap instance.
Moreover, we examine the model under an l_p objective which is a generalization of the standard Correlation Clustering objective, MinDisagree. We give an approximation algorithm and show an almost matching integrality gap for this objective.<br>
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Advisors: Yury Makarychev and Janos Simon<br>
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Committee Members: Yury Makarychev, Janos Simon, Konstantin Makarychev and Aaron Potechin<br>
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