Niko Välimäki will give a talk:

Title: Efficient Path Kernels for Reaction Function Prediction

Abstract: Kernels for structured data are rapidly becoming an essential part of the machine learning toolbox. Graph kernels provide similarity measures for complex relational objects, such as molecules and enzymes. Graph kernels based on walks are popular due their fast computation but their predictive performance is often not satisfactory, while kernels based on subgraphs suffer from high computational cost and are limited to small substructures. Kernels based on paths offer a promising middle ground between these two extremes. However, the computation of path kernels has so far been assumed computationally too challenging. In this presentation, we introduce an effective method for computing path based kernels; we employ a Burrows-Wheeler transform based compressed path index for fast and space-efficient enumeration of paths. In our experiments with chemical reaction graphs, path based kernels surpass state-of-the-art graph kernels in prediction accuracy.

Joint work with Markus Heinonen, Veli Mäkinen and Juho Rousu.