MLG graduate student Alessandro Ialongo has been selected for the 2017 Qualcomm Innovation Fellowship. He was awarded $40,000 for his innovation proposal titled “Learning and Decision-Making for Autonomous Behaviour”. The fellowship also involves the assignment of a Qualcomm researcher as mentor to facilitate close collaboration and interaction with Qualcomm Research & Development.
Author Archive for: admin
We wish to highlight three recent papers led by members from the group. The text can be found on the authors’ personal pages.
Mark Rowland, Aldo Pacchiano and Adrian Weller.
Conditions beyond treewidth for tightness of higher-order LP relaxations
International Conference on Artificial Intelligence and Statistics (AISTATS) 2017
Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo León-Villagrá, Zoubin Ghahramani and James Hensman.
GPflow: A Gaussian process library using TensorFlow
Journal of Machine Learning Ressearch (JMLR) 2017
Alexandre Navarro, Jes Frellsen and Richard Turner
The Multivariate Generalised von Mises distribution: Inference and Applications
Conference of the Association for Advancement of Artificial Intelligence (AAAI) 2017
Prof Sir David MacKay was an amazing man, who until recently was part of our group. David was a terrific researcher and teacher in machine learning, and a passionate campaigner for social good through his work on energy. He passed away tragically last year.
David was a fellow at Darwin College. With help from the Newton Trust, Darwin has established a research fellowship in David’s memory “…for research in any area drawing on mathematics and information theory, particularly those including applications in sustainability, policy or technology.”
We are delighted that Darwin has elected Adrian Weller as the first David MacKay Newton Research Fellow, to start in October.
The Leverhulme Centre for the Future of Intelligence (CFI; http://lcfi.ac.uk/) and the Machine Learning Group (http://mlg.eng.cam.ac.uk ) at the University of Cambridge invite applications for a Postdoctoral Research Associate in the study of trust and transparency in Artificial Intelligence (AI). The appointment will be for 3 years.
CFI is a new, highly interdisciplinary research centre addressing the challenges and opportunities posed by artificial intelligence (AI). Funded by the Leverhulme Trust, CFI is based at the University of Cambridge, with partners in the University of Oxford, Imperial College London, and UC Berkeley, and close links with industry.
This is a new Research Associate post within CFI’s Trust and Transparency project, based in central Cambridge. The post-holder will be a member of both CFI, and the Machine Learning Group, run by Prof Zoubin Ghahramani, in the Department of Engineering. This project, led by Dr Adrian Weller and involving partners at Imperial College, aims to develop processes to ensure that AI systems are transparent, reliable and trustworthy.
Deadline for applications is 19 December 2016. Please see http://lcfi.ac.uk/careers/postdoc-study-trust-transparency/ for further details.
The University values diversity and is committed to equality of opportunity.
The University has a responsibility to ensure that all employees are eligible to live and work in the UK.
Zoubin Ghahramani and Adrian Weller involved with the Centre for the Future of Intelligence, which was launched this week.
Zoubin, who introduced the talk, is the Deputy Director of the Centre.
Adrian is an Executive Fellow at the centre and is leading the project on Trust and Transparency, which is part of his general interest in the social implications of AI.
Professor Magaret Boden, who also spoke, said:
AI is hugely exciting. Its practical applications can help us to tackle important social problems, as well as easing many tasks in everyday life. And it has advanced the sciences of mind and life in fundamental ways. But it has limitations, which present grave dangers given uncritical use. CFI aims to pre-empt these dangers, by guiding AI development in human-friendly ways.
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.