- Alexander J. Smola (National ICT Australia/ANU, Canberra, Australia)
"Machine learning for web page ranking and collaborative filtering" (presentation slides[PDF])
- Jean-Philippe Vert (Centre for Computational Biology, ParisTech - Ecole des Mines de Paris, France)
"Statistical learning with graph kernels" (presentation slides[PDF])
Several problems in chemistry, in particular for drug discovery, can be formulated as classification or regression problems over molecules. These molecules, when represented by their planar structure, can be seen as labeled graphs. One approach to solve such problems is to apply kernel methods, such as support vector machines, with labeled graphs as training patterns. This requires an implicit embedding of the labeled graphs to a Hilbert space, carried out in practice through the definition of a positive definite function over labeled graph. In this talk I will review recent works that define such positive kernels. In particular we will see that although complete embeddings that separate non-isomorphic graphs, such as those obtained by counting all subgraphs or paths, are intractable in practice, fast approximations based on finite and infinite walk enumeration can be computed in polynomial time. These walk kernels and their variants give promising results on several benchmarks in computational chemistry.
- Paul Sheridan and Hidetoshi Shimodaira (Department of Mathematical and Computing Sciences, Tokyo Institute of Technology)
"Scale-free network priors in Bayesian inference with applications to Bioinformatics"