You may recall my previous post on Topological Data Analysis. I am happy to report that, in the interest of helping to advance this area of research, we (= Sivaraman Balakrishnan, Alessandro Rinaldo, Don Sheehy, Aarti Singh and me) are organizing a workshop on the topic. (To be honest, Sivaraman is doing all the hard work.)
Here are the details:
Call for Papers: Algebraic Topology and Machine Learning
In conjunction with Neural Information Processing Systems (NIPS 2012)
December 7 or 8 (TBD), 2011, Lake Tahoe, Nevada, USA.
Topological methods and machine learning have long enjoyed fruitful interactions as evidenced by popular algorithms like ISOMAP, LLE and Laplacian Eigenmaps which have been borne out of studying point cloud data through the lens of topology/geometry. More recently several researchers have been attempting to understand the algebraic topological properties of data. Algebraic topology is a branch of mathematics which uses tools from abstract algebra to study and classify topological spaces. The machine learning community thus far has focused almost exclusively on clustering as the main tool for unsupervised data analysis. Clustering however only scratches the surface, and algebraic topological methods aim at extracting much richer topological information from data.
The goals of this workshop are:
- To draw the attention of machine learning researchers to a rich and emerging source of interesting and challenging problems.
- To identify problems of interest to both topologists and machine learning researchers and areas of potential collaboration.
- To discuss practical methods for implementing topological data analysis methods.
- To discuss applications of topological data analysis to scientific problems.
We also invite submissions in a variety of areas, at the intersection of algebraic topology and learning, that have witnessed recent activity. Areas of focus for submissions include but are not limited to:
- Statistical approaches to robust topological inference.
- Novel applications of topological data analysis to problems in machine learning.
- Scalable methods for topological data analysis.
Submission. Submissions should be an extended abstract not exceeding 4 pages (excluding references) in NIPS format. Style files and other instructions are available at:
Please email all submissions to: email@example.com
The deadline for submissions is: September 16th, 2012 Organizers will review, select submissions and notify authors by Oct 7, 2012. Accepted submissions will be presented as a talk (20 min) or a poster.
Sep 16 – Deadline for submissions
Oct 7 – Notification of acceptance
Dec 8 (Tentative) – Workshop
Registration: Participants should refer to the NIPS 2012 website
for information on how to register for the workshop. Please direct any questions or comments to the organizers at firstname.lastname@example.org
Sivaraman Balakrishnan, Carnegie Mellon University
Alessandro Rinaldo, Carnegie Mellon University
Don Sheehy, INRIA Saclay – Ile-de-France
Aarti Singh, Carnegie Mellon University
Larry Wasserman, Carnegie Mellon University