Difference between revisions of "SOCR JMM 2026"
(→SOCR News & Events: 2026 JMM/AMS Special Session on Mathematical Foundation of Machine Learning) |
(→Overview) |
||
| Line 7: | Line 7: | ||
* ''Location'': Room 204C, [https://eventsdc.com/venue/walter-e-washington-convention-center Walter E. Washington Convention Center], 801 Allen Y. Lew Place NW, Washington, DC 20001 | * ''Location'': Room 204C, [https://eventsdc.com/venue/walter-e-washington-convention-center Walter E. Washington Convention Center], 801 Allen Y. Lew Place NW, Washington, DC 20001 | ||
| − | * ''Organizers'': Maryam Bagherian & Emanuele Zappala (Idaho State University) | + | * ''Organizers'': [https://www.isu.edu/math/people/tenure/ Maryam Bagherian & Emanuele Zappala (Idaho State University)] |
* ''Abstract'': This special session focuses on the rigorous mathematical foundations underlying modern machine learning. Topics include, but are not limited to, operator theory, functional analysis, optimization, linear/multilinear algebra, metric learning, and approximation theory of neural networks. We welcome contributions that deepen understanding of data-driven algorithms through fundamental mathematical inquiry, emphasizing theoretical rigor in exploring the principles driving machine learning. | * ''Abstract'': This special session focuses on the rigorous mathematical foundations underlying modern machine learning. Topics include, but are not limited to, operator theory, functional analysis, optimization, linear/multilinear algebra, metric learning, and approximation theory of neural networks. We welcome contributions that deepen understanding of data-driven algorithms through fundamental mathematical inquiry, emphasizing theoretical rigor in exploring the principles driving machine learning. | ||
| − | |||
== Abstract Submission== | == Abstract Submission== | ||
Revision as of 09:06, 13 August 2025
Contents
SOCR News & Events: 2026 JMM/AMS Special Session on Mathematical Foundation of Machine Learning
Overview
- Location: Room 204C, Walter E. Washington Convention Center, 801 Allen Y. Lew Place NW, Washington, DC 20001
- Abstract: This special session focuses on the rigorous mathematical foundations underlying modern machine learning. Topics include, but are not limited to, operator theory, functional analysis, optimization, linear/multilinear algebra, metric learning, and approximation theory of neural networks. We welcome contributions that deepen understanding of data-driven algorithms through fundamental mathematical inquiry, emphasizing theoretical rigor in exploring the principles driving machine learning.
Abstract Submission
- Abstract Submission: Abstracts must be submitted through the AMS portal.
- Submission Period: July 10, 2025 -- Tuesday, September 9, 2025
Program
... pending ...
Ivo Dinov's talk
Translate this page:
