Approximation Theory and Machine Learning
Purdue University, September 29 - 30, 2018
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.
- 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, John Hopkins University
- Yusu Wang, Ohio State University
- Stefan Wild, Argonne National Lab
- Greg Buzzard, Department of Mathematics, Purdue University
- David Gleich, Department of Computer Science, Purdue University
- Guang Lin, Department of Mathematics, Purdue University
Questions about registration can be addressed to Anna Hook (email@example.com).
Conference funded by IMA conference grant, Purdue College of Science, Purdue Department of Mathematics and Purdue Department of Computer Science.