September 16, 2012 – Submissions Due
October 7, 2012 – Acceptance Notices
December 7 or 8 (TBD), 2011, Lake Tahoe, Nevada, USA.
From the call for papers:
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.
NIPS2012 site. You will appreciate the “dramatization.” 😉
Put on your calendar and/or watch for papers!