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  <title><![CDATA[ARC Colloquium:  Xiaorui Sun (Microsoft)]]></title>
  <body><![CDATA[<p align = "center"><strong>Algorithms &amp; Randomness Center (ARC)</strong></p>

<p align = "center"><strong>Xiaorui Sun&nbsp;(Microsoft Research)</strong></p>

<p align = "center"><strong>Monday, March 12, 2018</strong></p>

<p align = "center"><strong>Klaus 1116 East - 11am</strong></p>

<p>&nbsp;</p>

<p><strong>Title:</strong>&nbsp;&nbsp; The Query Complexity of Graph Isomorphism: Bypassing Distribution Testing Lower Bounds</p>

<p><strong>Abstract:&nbsp; </strong> &nbsp;</p>

<p>We study the edge query complexity of graph isomorphism in the property testing model for dense graphs. We give an algorithm that makes n^{1+o(1)} queries, improving on the previous best bound of O~(n^{5/4}). Since the problem is known to require \Omega(n) queries, our algorithm is optimal up to a subpolynomial factor.</p>

<p>While trying to extend a known connection to distribution testing, discovered by Fischer and Matsliah (SICOMP 2008), one encounters a natural obstacle presented by sampling lower bounds such as the $\Omega(n^{2/3})$-sample lower bound for distribution closeness testing (Valiant, SICOMP 2011). In the context of graph isomorphism testing, these bounds lead to an $n^{1+\Omega(1)}$ barrier for Fischer and Matsliah&#39;s approach. We circumvent these limitations by exploiting a geometric representation of the connectivity of vertices. An approximate representation of similarities between vertices can be learned with a near-linear number of queries and allows relaxed versions of sampling and distribution testing problems to be solved more efficiently.</p>

<p>Joint work with Krzysztof Onak</p>

<p>--------------------------------------</p>

<p><a href="http://www.cs.columbia.edu/~xiaoruisun/">Speaker&#39;s webpage</a></p>

<p><em>Videos of recent talks are available at: </em><a href="https://smartech.gatech.edu/handle/1853/46836"><em>https://smartech.gatech.edu/handle/1853/46836</em></a></p>

<p><a href="https://mailman.cc.gatech.edu/mailman/listinfo/arc-colloq"><em>Click here to subscribe to the seminar email list: arc-colloq@cc.gatech.edu </em></a></p>

<p>&nbsp;</p>
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