links

Select Lab ML Department

Secondary affiliation I am also afflilated with the MPI for Biological Cybernetics as a research scientist.
MPI homepage

Recent work • Kernel choice and classifiability for RKHS embeddings of probability distributions, NIPS 2009, oral, honorable mention for outstanding student paper
• A Fast, Consistent Kernel Two-Sample Test , NIPS 2009, spotlight
• Nonlinear directed acyclic structure learning with weakly additive noise models , NIPS 2009
• Temporal Kernel CCA and its Application in Multimodal Neuronal Data Analysis , Machine Learning, 2009

Recent talks

• Introduction to Independent Component Analysis
  Independent component analysis (ICA) is a technique for extracting underlying sources of information from linear mixtures of these sources, based only on the assumption that the sources are independent of each other. To illustrate the idea, we might be in a room containing several people (the sources) talking simultaneously, with microphones picking up multiple conversations at once (the mixtures), and we might wish to automatically recover the original separate conversations from these mixtures. More broadly, ICA is used in a very wide variety of applications, including signal extraction from EEG, image processing, bioinformatics, and economics. I will present an introduction to ICA, which includes a description of principal component analysis (PCA), and how ICA differs from PCA, the maximum likelihood approach, the case where fixed nonlinearities are used as heuristics for source extraction, some more modern information theoretic approaches, and a kernel-based method. I will also cover two optimization strategies, and provide a comparison of the various approaches on benchmark data, to reveal the strengths and failure modes of different ICA algorithms (with a focus on modern, recently published methods).
  
  Talk Slides
  
  Software for Fast Kernel ICA demo
  
  MLD student research symposium
  
  

Earlier talks

Slides from earlier talks may be found here. I also have talks on Videolectures, but I have since given much better presentations on these topics (e.g. the ICML tutorial).