links
Secondary affiliation
I am also afflilated with the MPI for Biological Cybernetics as a research scientist.
MPI homepage
Recent work
KPC , Software to implement Nonlinear directed acyclic structure learning with weakly additive noise models (by Robert Tillman)
Consistent Nonparametric Tests of Independence , JMLR 2010
Hilbert Space Embeddings and Metrics on Probability Measures , JMLR 2010
Discussion of: Brownian distance covariance , Ann. App. Stat. 2009
Nonparametric Tree Graphical Models , AISTATS 2010
workshops (2009)
- NIPS 2009 Workshop on Temporal Segmentation
Data with temporal (or sequential) structure arise in several applications, such as speaker diarization, human action segmentation, network intrusion detection, DNA copy number analysis, and neuron activity modelling, to name a few. The purpose of this workshop is to bring together experts working on temporal segmentation from the statistics, machine learning, and signal processing communities, to address a broad range of applications from robotics to neuroscience, and to define the current and future challenges in the field.
Web page
- NIPS 2009 Workshop on Large-Scale Machine Learning: Parallelism and Massive Datasets
Prior NIPS workshops have focused on the topic of scaling machine learning, which remains an important developing area. We introduce a new perspective by focusing on how large-scale machine learning algorithms should be informed by future parallel architectures. By bringing together experts from computer architectures, parallel algorithms, and scientific computing, and machine learning we will develop a concrete research agenda for large-scale learning on parallel architectures.
Web page
workshops (2004-2008)
- NIPS 2008 Workshop on Kernel Learning: Automatic Selection of Optimal Kernels
Videolectures
Web page
- NIPS 2007 Workshop on Representations and Inference on Probability Distributions
Videolectures
Web page
- NIPS 2005 Workshop on Kernel Methods and Structured Domains
Videolectures
Web page
- NIPS 2004 Workshop on Learning with Structured Outputs