Connectionists: CFP: ICML 2014 Workshop on Topological Methods for Machine Learning

[Jerry Zhu]

Call for Papers
ICML Workshop on Topological Methods for Machine Learning
June 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: 3/21/14
   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.