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UID:75a9b2b5846ac671acd6d349b02036e9
CATEGORIES:Experimental Mathematics Seminar
CREATED:20240325T210523
SUMMARY:Reinforcement learning and pattern finding in combinatorics
LOCATION:https://rutgers.zoom.us/j/94346444480 [password: The 20th Catalan number\, 
 alias (40)!/(20!*21!)
DESCRIPTION:We will look at two ways we can use tools from machine learning to help us 
 with research in combinatorics. First we discuss reinforcement learning, a 
 method that gives us a way to check conjectures for counterexamples efficie
 ntly. While it usually does not perform as well as other simpler methods, t
 here have been several examples of projects in the past few years where RL 
 was crucial for success. In the second half of the talk we will consider th
 e following question of Ellenberg: at most how many points can we pick in t
 he N by N grid, without creating an isosceles triangle? The best known cons
 tructions, found by computer searches for small values of N, clearly follow
  a pattern which we do not yet understand. We will discuss how one can trai
 n transformers to understand this pattern, and use this trained transformer
  to help us find a bit better constructions for various N. This is joint wo
 rk with Jordan Ellenberg, Marijn Heule, and Geordie Williamson\n
X-ALT-DESC;FMTTYPE=text/html:<p>We will look at two ways we can use tools from machine learning to help 
 us with research in combinatorics. First we discuss reinforcement learning,
  a method that gives us a way to check conjectures for counterexamples effi
 ciently. While it usually does not perform as well as other simpler methods
 , there have been several examples of projects in the past few years where 
 RL was crucial for success. In the second half of the talk we will consider
  the following question of Ellenberg: at most how many points can we pick i
 n the N by N grid, without creating an isosceles triangle? The best known c
 onstructions, found by computer searches for small values of N, clearly fol
 low a pattern which we do not yet understand. We will discuss how one can t
 rain transformers to understand this pattern, and use this trained transfor
 mer to help us find a bit better constructions for various N. This is joint
  work with Jordan Ellenberg, Marijn Heule, and Geordie Williamson</p>
CONTACT:Adam Zsolt Wagner, Worcester Polytechnic Institute
DTSTAMP:20260829T192935
DTSTART;TZID=America/New_York:20240328T170000
DTEND;TZID=America/New_York:20240328T180000
SEQUENCE:0
TRANSP:OPAQUE
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