MLG members are involved with the organisation of one symposium and three workshops at NIPS. These are:
Machine Learning and the Law
Adrian Weller, Thomas D. Grant, Conrad McDonnell and Jat Singh
Bayesian Deep Learning
Yarin Gal, Christos Louizos, Zoubin Ghahramani, Kevin P Murphy and Max Welling
Towards an Artificial Intelligence for Data Science
Charles Sutton, James Geddes, Zoubin Ghahramani, Padhraic Smyth and Chris Williams
Reliable machine learning in the wild
Jacob Steinhardt, Dylan Hadfield-Menell, Adrian Weller, David Duvenaud, Percy Liang
MLG members are shown in bold. A full schedule for the NIPS workshops and symposia can be found here.
Five new papers from the group are to appear at the 2016 conference on Advances in Neural Information Processing Systems (NIPS 2016), to be held in December in Barcelona, Spain.
The list of papers are:
Renyi Divergence Variational Inference
Yingzhen Li and Rich Turner
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
Yarin Gal and Zoubin Ghahramani
Understanding Probabilistic Sparse Gaussian Process Approximations
Matthias Stephan Bauer, Mark van der Wilk, and Carl Edward Rasmussen.
Consistent Kernel Mean Estimation for Functions of Random Variables
Adam Ścibior*, Carl-Johann Simon-Gabriel*, Iliya Tolstikhin, Bernhard Schölkopf. * equal contribution.
Distributed Flexible Nonlinear Tensor Factorization
Shandian Zhe, Kai Zhang, Pengyuan Wang, Kuang-chih Lee, Zenling Xu, Yuan Qi, and Zoubin Ghahramani.
For the most up to date versions of the papers, visit the authors’ webpages, which may be found through our group members page.
Alex Matthews has been given a software award from the Google Open Source Programs Office for his work on TensorFlow. The award is for “…people outside of Google that they thought were doing great things in the world of open source”, in particular for his work implementing Cholesky backpropagation. The work removes one of the main barriers to implementing Gaussian processes in TensorFlow and is part of the ongoing GPflow project.
Ten papers involving authors from MLG will appear at the International Conference on Machine Learning 2016. They are:
Unitary Evolution Recurrent Neural Networks
Martin Arjovsky, Amar Shah and Yoshua Bengio
Predictive Entropy Search for Multi-objective Bayesian Optimization
Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Amar Shah and Ryan P. Adams.
Pareto Frontier Learning with Expensive Correlated Objectives
Amar Shah and Zoubin Ghahramani
Continuous Deep Q-Learning with Model-based Acceleration
Shixiang Gu, Timothy Lillicrap, Ilya Sutskever and Sergey Levine.
Black-box alpha-divergence Minimization.
José Miguel Hernández-Lobato*, Yingzhen Li*, Mark Rowland, Daniel Hernández-Lobato, Thang Bui and Richard E. Turner.
(* joint first author)
Deep Gaussian Processes for Regression using Approximate Expectation Propagation.
Thang Bui, Daniel Hernández-Lobato, Yingzhen Li, José Miguel Hernández-Lobato and Richard E. Turner.
Scalable Discrete Sampling as a Multi-Armed Bandit Problem.
Yutian Chen and Zoubin Ghahramani
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Yarin Gal and Zoubin Ghahramani
Uprooting and Rerooting Graphical Models
Train and Test Tightness of LP Relaxations in Structured Prediction
Ofer Meshi, Mehrdad Mahdavi, Adrian Weller and David Sontag
For more details see the author’s personal webpages.
We are delighted to announce that Dr. José Miguel Hernández-Lobato will join Cambridge MLG as a University Lecturer in Machine Learning later this year. Miguel joins us from Harvard and has a broad range of interests in probabilistic machine learning. For more details see his website.
Cambridge Machine Learning Group has a strong involvement with the new Alan Turing Institute. The ATI is the national institute for research in Data Science and is headquartered in London. It brings together researchers from five top British universities: the Universities of Cambridge, Edinburgh, Oxford, and Warwick, and University College London.
Our own Prof. Zoubin Ghahramani has been appointed University of Cambridge Liason Director.
Group members Prof. Carl Rasmussen and Dr. Adrian Weller have been appointed as ATI Faculty Fellows.
Well done to Yarin Gal who has been awarded the Michael and Morven Heller Research Fellowship at St Catherine’s College, Cambridge.
The fellowship is awarded to
“… exceptional candidates in the field of Computer Science including those who will apply Computer Science to ‘big data’ in all fields including biomedical sciences generally.”
Microsoft Research, Cambridge and the University of Cambridge Machine Learning Group have organized a Machine Learning and Artificial Intelligence workshop on Friday March 18th 2016 at the public facilities of Microsoft Research Cambridge. The day will feature talks from both research groups and representatives of local startups.
Attendance is open to the public but requires prior registration. More details, including a schedule for the day can be found here.
We are seeking up to three highly creative and motivated Postdoctoral Research Assistants/Associates to join the Machine Learning Group in the Department of Engineering, University of Cambridge, UK. All positions will involve research in direct collaboration with Professor Zoubin Ghahramani.
Post 1: Building an Automatic Statistician and Probabilistic Programming. This post holder will be working on developing algorithms for the automated analysis and interpretation of data in collaboration with MIT. Candidates should have extensive experience in probabilistic modelling, scalable approximate inference, and existing probabilistic programming languages. See: http://www.automaticstatistician.com/
Post 2: Bayesian Nonparametrics. This post holder will be working with foundations, models and inference algorithms for Bayesian Nonparametrics. Candidates should have research experience in Bayesian nonparametrics and MCMC methods.
Post 3: Deep Probabilistic Models for Making Sense of Unstructured Data. This post holder will be working on deep learning, probabilistic modelling and privacy-preserving machine learning in a collaboration between Professor Ghahramani and Professor Neil Lawrence’s group.
The successful applicants will have or be near completing a PhD in computer science, information engineering, statistics or a related area, and will have extensive research experience and a strong publication record in machine learning, including ideally papers in top machine learning conferences such as NIPS, UAI, ICML, and AISTATS.
If you have any questions about this vacancy or the application process, please contact Miss Diane Hazell, email: email@example.com, Tel: +44 01223 748529. To apply you must upload your information via: http://www.jobs.cam.ac.uk/job/9649/
The deadline for applications is 6 April 2016.
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