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Foundations of machine learning and statistics; Bayesian nonparametric statistics; computable probability theory; probabilistic programming languages.
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Daniel Roy Dan Roy aka daniel roy, daniel m roy, droy Research Director , CIFAR Canada AI Chair , and Founding Faculty , Vector Institute Associate Professor Department of Statistical Sciences Department of Computer Science (cross-appointment) University of Toronto Department of Computer and Mathematical Sciences University of Toronto Scarborough Editorial Boards Action Editor, Journal of Machine Learning Research Action Editor, Transactions of Machine Learning Research Jump to: Teaching | Research Articles | Group Members | Probabilistic Programming | Contact research My research is focused on the foundational principles underlying prediction, inference, and decision making under uncertainty. This focus on foundations ties together multiple lines of work, which span machine learning, statistics, mathematical logic, applied probability, and computer science. My group has made significant contributions to learning theory, statistical net...
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36 - statistics.utoronto.ca, ece.utoronto.ca, utsc.utoronto.ca, oc-vp-distribution03.ethz.ch, conferences.au.dk, emma.cam.ac.uk, cbl.eng.cam.ac.uk, learning.eng.cam.ac.uk, jun.sites.ku.dk, math.u-psud.fr, hdl.handle.net, cam.ac.uk, eng.cam.ac.uk, mlg.eng.cam.ac.uk, gatsby.ucl.ac.uk, scholar.google.ca, cs.ox.ac.uk, validator.w3.org, jigsaw.w3.org, papers.nips.cc, dx.doi.org
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