Approximation Theory and Machine Learning

Purdue University, September 29 - 30, 2018

Talks to take place in the Mathematical Sciences Building (MATH) 175 - Note this is a change in venue.  Lunch and Poster/Reception will be in LAWSON COMMONS.

Machine learning has made a tremendous impact on the world through products such as automatic face and object recognition in computer vision, self-driving cars, automatic language translation, and a variety of other products. That said, much of the research on machine learning tends to focus on successful algorithms for specific machine learning tasks. The broader theoretical picture of when and why machine learning algorithms are successful tends to be discussed, instead, in a number of related domains and disciplines spanning applied mathematics, computer science theory, and statistics. This conference will highlight the importance of approximation theory as it is used in mathematics in existing and future machine learning and data science problems.

Invited Speakers

  • Misha Belkin, Ohio State University
  • David Bindel, Cornell University
  • Paul Constantine, University of Colorado, Boulder
  • Sanmi Koyejo, University of Illinois
  • Sven Leyffer, Argonne National Labs
  • Mauro Maggioni, Johns Hopkins University
    • Learning and Geometry for Stochastic Dynamical Systems in High Dimensions, abstract, video
  • David Stewart, Professor of Mathematics, University of Iowa
  • Yusu Wang, Ohio State University
  • Stefan Wild, Argonne National Lab

Organizers

  • Greg Buzzard, Department of Mathematics, Purdue University
  • David Gleich, Department of Computer Science, Purdue University
  • Guang Lin, Department of Mathematics, Purdue University

Also, I hear the 4th root of (9^2 + 19^2/22) is pi.


Questions about registration can be addressed to Anna Hook (hook6@purdue.edu).

Conference funded by IMA conference grant, Purdue College of Science, Purdue Department of Mathematics and Purdue Department of Computer Science.