Finale Doshi-Velez, MLG group alumnus, has been named among IEEE Intelligent Systems Magazine’s “AI’s 10 to Watch”!   From the IEEE Computer Society website:

Every two years, IEEE Intelligent Systems acknowledges and celebrates 10 young stars in the field of AI as “AI’s 10 to Watch.” These accomplished researchers have all completed their doctoral work in the past five years. Despite being relatively junior in their career, each one has made impressive research contributions and had an impact in the literature — and in some cases, in real-world applications as well.

Finale is now a postdoctoral research fellow at the Harvard School of Engineering and Applied Sciences and the Harvard Medical School.

More on the story article here:  http://www.computer.org/portal/web/computingnow/AI-s-10-to-Watch-2013
Harvard’s news story:  http://hips.seas.harvard.edu/content/finale-doshi-velez-named-among-ais-10-watch-0

Novi Quadrianto, Sébastien Bratières, and Zoubin Ghahramani have been awarded an Amazon Web Services (AWS) in Education Research Grant (in the Machine Learning category) in the amount of 10,000 USD in AWS credit for their project on “Large Scale Bayesian Non-parametric Structured Prediction”.

Information about the grant can be found here.

We are pleased to announce the Cambridge-Tübingen PhD fellowships between the University of Cambridge Machine Learning Group and the Max Planck Institute for Intelligent Systems Empirical Inference Department in Tübingen.  These fellowships will be co-supervised by Prof. Zoubin Ghahramani in Cambridge and Prof. Bernhard Schoelkopf in Tübingen.

Please see the fellowship webpage and our PhD Admissions FAQ page for more information.

Three new papers from the group are to appear in the Proceeding of Neural Information Processing Systems, 2013, and will be presented at the NIPS conference in Lake Tahoe, USA this December.  The papers are:

• R. Frigola, F. Lindsten, T. B. Schön and C. E. Rasmussen.  Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC.
• D. Hernández-lobato, J. M. Hernández-Lobato.  Learning Feature Selection Dependencies in Multi-task Learning.
• J. M. Hernández-Lobato, J. R. Lloyd, D. Hernández-lobato.  Gaussian Process Conditional Copulas with Applications to Financial Time Series.

More information can be found on our publications page.

Richard Turner’s work on developing rich and efficient machine learning methods for audio data with applications ranging from intelligent hearing devices to audio restoration is covered by Wired Magazine and Cambridge Research Horizons Magazine, and is interviewed by Click on the BBC Radio World Service.

Links:

Wired Magazine article: link

Cambridge Research Horizons Magazine article: link

BBC Radio interview recording: link

The 2013 Classic Paper Prize at the International Conference on Machine Learning (ICML) was won by Zoubin Ghahramani and coauthors Xiaojin Zhu and John Lafferty for their 2003 paper “Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions” . The Classic Paper Prize is given to the paper  published at ICML 10 years previously which has had the most impact on the field. Semi-supervised learning refers to the problem of combining small amounts of labelled data (i.e. supervised learning) with large amounts of unlabelled data (i.e. unsupervised learning).
This 2003 paper, which has now been cited over 1400 times, developed a simple and highly-scalable graph-based method for semi-supervised classification, and related it to harmonic functions, random walks, electric networks, and spectral graph theory. Graph-based semi-supervised learning has now become a standard approach for combining labelled and unlabelled data in many application domains.

Link to the paper: http://mlg.eng.cam.ac.uk/zoubin/papers/zgl.pdf
Link to the ICML conference: http://icml.cc/2013/
Link to the Machine Learning group website: http://mlg.eng.cam.ac.uk/

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 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.