NIPS 2004 Workshop

Graphical Models and Kernels


Graphical models provide a natural method to model variables with structured conditional independence properties. They allow for understandable description of models, making them a popular tool in practice. Kernel methods excel at modeling data which need not be structured at all, by using mappings into high-dimensional spaces (also popularly called the kernel trick). The popularity of kernel methods is primarily due to their strong theoretical foundations and the relatively simple convex optimization problems.

Recent progress towards a unification of the two areas has seen work on Maximum Margin Markov Networks, structured output spaces, and kernelized Conditional Random Fields. Some work has also been done on using fundamental properties of the exponential family of probability distributions to establish links.

The aim of this workshop is to bring together researchers from both the communities together in order to facilitate interactions. More specifically, the issues we want to address include (but are not limited to), the fundamental theory linking these fields. We want to investigate connections using exponential families, conditional random fields, Markov models etc. We also wish to explore the applications of the kernel trick to graphical models and study the optimization problems which arise out of such a marriage. Uniform convergence type results for theoretically bounding the performance of such models will also be discussed.

Date and Location

December 17, 2004, Whistler, B.C., Canada

Getting in Touch

If you are interested in contributing, please send e-mail to Alex Smola, Ben Taskar, or Vishy Vishwanathan.