Topology 2014 - Workshop on Topological Methods for Machine Learning

ICML Workshop on Topological Methods for Machine Learning
June 25, 2014, Beijing, China
http://topology.cs.wisc.edu
This workshop aims to translate advances in computational topology (e.g., homology, cohomology, persistence, Hodge theory) into machine learning algorithms and applications. Topology has the potential to be a new mathematical tool for machine learning. We expect the workshop to bring topologists, statisticians and machine learning researchers closer to realize this potential.
Computational topology saw three major developments in recent years: persistent homology, Euler calculus and Hodge theory. Persistent homology extracts stable homology groups against noise; Euler Calculus encodes integral geometry and is easier to compute than persistent homology or Betti numbers; Hodge theory connects geometry to topology via optimization and spectral method. All three techniques are related to Morse theory, which is inspiring new computational tools or algorithms for data analysis. Computational topology has inspired a number of applications in the last few years, including game theory, graphics, image processing, multimedia, neuroscience, numerical PDE, peridynamics, ranking, robotics, voting theory, sensor networks, and natural language processing.
Which promising directions in computational topology can mathematicians and machine learning researchers work on together, in order to develop new models, algorithms, and theory for machine learning? While all aspects of computational topology are appropriate for this workshop, our emphasis is on topology applied to machine learning -- concrete models, algorithms and real-world applications.
Topics
We seek papers in all areas where topology and machine learning interact, especially on translating computational topology into new machine learning algorithms and applications. Topics include, but are not limited to, the following:
- Models in machine learning where topology plays an important role;
- Applications of topology in all areas related to machine learning and human cognition;
- Statistical properties for topological inference;
- Algorithms based on computational topology;
- Feature extraction with topological methods.
Submissions
Papers should be 4-page (excluding references) extended abstracts on topics relevant to the workshop.
Papers must be formatted in ICML style following this webpage: http://icml.cc/2014/14.html.
Please email PDF submissions to topologyicml2014-AT-gmail.com.
Submissions due date: 4/4/14 (extended)
Authors notification: 4/18/2014
Organizers
Lek-Heng Lim, University of Chicago
Yuan Yao, Peking University
Jerry Zhu, University of Wisconsin-Madison
Jun Zhu, Tsinghua University
Questions and comments can be directed to topologyicml2014-AT-gmail.com.