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You are here: Cambridge Machine Learning Group / 2013 / April

Archive for month: April, 2013

Christian Steinruecken interviews with The Naked Scientists

19 Apr 2013 / Comments Off / in News/by admin

Group post-doc Christian Steinruecken radio interviews with The Naked Scientists, an award-winning BBC weekly radio programme delivered by a University-based group focusing on broad topics in science for a general audience.  Christian spoke to The Naked Scientists about data compression, some basics of how it works, and its role in the technologies that we employ today.

Learn more about The Naked Scientists and their programme on their webpage.

Information about Christian and his research can be found on his webpage.

The department press release with some excerpts from the interview here.

The original interview can be found here.

You can listen to the interview as an mp3 here.

Eight new papers from the group to appear in ICML 2013

18 Apr 2013 / Comments Off / in News/by admin

Eight new papers from the group are to appear in the proceedings of the 30th International Conference on Machine Learning (ICML 2013), to be held in Altanta, Georgia, USA in June.  ICML is a leading conference on machine learning.  Here are the list of papers with links to the documents:

  • D Duvenaud, JR Lloyd, R Grosse, JB Tenenbaum, and Z Ghahramani.  Structure Discovery in Nonparametric Regression through Compositional Kernel Search.  [arXiv]
  • E Gilboa, Y Saatci, and JP Cunningham.  Scaling multidimensional Gaussian Processes using projected additive approximations.  [arXiv]
  • C Heaukulani, and Z Ghahramani.  Dynamic Probabilistic Models for Latent Feature Propagation in Social Networks.  [pdf]
  • B Lakshminarayanan, DM Roy, YW Teh.  Top-down particle filtering for Bayesian decision trees.  [arXiv]
  • D Lopez-Paz, JM Hernandez-Lobato, and Z Ghahramani.  Gaussian process vine copulas for multivariate dependence.  [pdf]
  • C Reed and Z Ghahramani.  Scaling the Indian Buffet Process via Submodular Maximization.  [arXiv]
  • AG Wilson and RP Adams.  Gaussian Process Covariance Kernels for Pattern Discovery and Extrapolation.  [arXiv]
  • Y Wu, JM Hernandez-Lobato, and Z Ghahramani.  Dynamic Covariance Models for Multivariate Financial Time Series.

Abstracts and additional material can be found on our publications page, and links to author webpages can be found on our group members page.

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