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Sat 23 Jan 2016 15:30 - 16:30 at Room St Petersburg II - Poster Session

We propose to write denotational semantics for a probabilistic programming language in terms of reproducing kernel Hilbert spaces for characteristic kernels. This opens up possibilities for providing convergence guarantees for approximate expansions, as well as practical advantages of using kernel methods for machine learning. At the moment we only write semantics for a simple language for probabilistic expressions, but with time we hope to extend it to general probabilistic programs with conditioning.

Sat 23 Jan

pps-2016
15:30 - 16:30: PPS 2016 - Poster Session at Room St Petersburg II
pps-201615:30 - 16:30
Meeting
Aleksey KligerXamarin, Inc., Sean StromstenBAE Systems, Inc.
Pre-print
pps-201615:30 - 16:30
Meeting
Theophilos GiannakopoulosBAE Systems, Inc., Mitchell WandNortheastern University, Andrew CobbNortheastern University
Pre-print
pps-201615:30 - 16:30
Meeting
Larry MossIndiana University, Chung-chieh ShanIndiana University, Alexandra SilvaRadboud University Nijmegen
Pre-print
pps-201615:30 - 16:30
Meeting
Faris Abou-SalehUniversity of Oxford, Kwok-Ho CheungUniversity of Oxford, Jeremy GibbonsUniversity of Oxford, UK
Pre-print
pps-201615:30 - 16:30
Meeting
Johannes HölzlTechnische Universität München
Pre-print
pps-201615:30 - 16:30
Meeting
Yohei MiyamotoGraduate School of Informatics, Kyoto University, Kohei Suenaga, Koji NakazawaGraduate School of Information Science, Nagoya University
Pre-print
pps-201615:30 - 16:30
Meeting
Ryan CulpepperNortheastern University
Pre-print
pps-201615:30 - 16:30
Meeting
Pre-print
pps-201615:30 - 16:30
Meeting
Adam ŚcibiorUniversity of Cambridge, Andrew D. GordonMicrosoft Research and University of Edinburgh
Pre-print
pps-201615:30 - 16:30
Meeting
Adam ŚcibiorUniversity of Cambridge, Bernhard SchölkopfMPI Tuebingen
Pre-print