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<p class="MsoNormal"><i><span style="font-family:Helvetica;color:#8B0102"> </span></i><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-family:Helvetica;color:#8B0102">UNIVERSITY OF CHICAGO</span></i><o:p></o:p></p>
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<i><span style="font-family:Helvetica;color:#8B0102">COMPUTER SCIENCE DEPARTMENT</span></i><o:p></o:p></p>
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<i><span style="font-family:Helvetica;color:#8B0102">PRESENTS</span></i><o:p></o:p></p>
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<b><span style="font-family:Helvetica;color:#212121">Leonardo Coregliano, PhD</span></b><o:p></o:p></p>
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<b><span style="color:#212121">University of Chicago</span></b><o:p></o:p></p>
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<span style="font-size:10.0pt;font-family:"Calibri",sans-serif;color:#212121"> <img width="192" height="192" style="width:2.0in;height:2.0in" id="Picture_x0020_2" src="cid:image001.jpg@01DA84DE.BF3B6D80" alt="A person in a blue shirt
Description automatically generated"></span><o:p></o:p></p>
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<b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:black;background:yellow">Tuesday, April 9, 2024 at 3:30pm</span></b><o:p></o:p></p>
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<b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:black;background:yellow">Room – Kent 107</span></b><o:p></o:p></p>
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<p class="MsoNormal"><b><i>Title:</i></b> <span style="font-family:"Calibri",sans-serif">
High-arity PAC learning via exchangeability<br>
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<b><i>Abstract:</i></b> <span style="font-family:"Calibri",sans-serif">Classic PAC learning theory studies when we can make an accurate guess of a set based on finitely<br>
many i.i.d.\ samples from it. The Fundamental Theorem of Statistical Learning characterizes when<br>
such an accurate guess can be made in terms of the Vapnik--Chervonenkis dimension. The natural<br>
generalization of PAC learning functions has also been characterized in terms of the Natarajan<br>
dimension (when the co-domain is finite) and in terms of the Daniely--Shalev-Shwartz dimension (for<br>
arbitrary co-domains).<br>
<br>
In this talk, we will explore a different generalization, called high-arity PAC learning, that is<br>
motivated by PAC learning of graphs, hypergraphs and relational structures and relies on<br>
exchangeability theory. We will cover the basic definitions, the statement of the high-arity<br>
Fundamental Theorem, some proof ideas and mention the furthest reaches of the theory covering "PAC<br>
learning for (quasi)random graphs".<br>
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No prior knowledge of learning theory or exchangeability theory will be required.<br>
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This talk is based on joint work with Maryanthe Malliaris.</span><o:p></o:p></p>
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<p class="MsoNormal"><b><i>Bio: </i></b><span style="font-family:"Calibri",sans-serif">Leonardo Nagami Coregliano is an L.E. Dickson Instructor at The University of Chicago. He also<br>
received his Ph.D. in Mathematics and Computer Science at The University of Chicago, advised by<br>
Professor Alexander Razborov. His research focuses on interactions of combinatorics and model<br>
theory, with particular emphasis on asymptotic combinatorics and limit theory.</span><o:p></o:p></p>
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<p class="MsoNormal"><b><i>Host</i></b><b>: Alexander Razborov</b><o:p></o:p></p>
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