Based in the Department of Statistics at the University of Oxford, our research spans the whole range of modern statistics and machine learning with particular strengths in probabilistic modelling, nonparametric methods, Monte Carlo, variational inference, deep learning, causality, and applications in genetics, genomics and medicine.
On 25 May 2017 we will be hosting “Diversity in Machine Learning”, an event for undergraduates, featuring two great speakers, Stefanie Jegelka (MIT) and Raia Hadsell (DeepMind).
“Sparse graphs using exchangeable random measures” by François Caron and Emily Fox will be presented to the Royal Statistical Society at the Discussion Meeting on Wednesday, May 10th at 5pm. More information here.
Three papers from the group have been accepted at AISTATS 2017 and one paper at ICLR 2017.
The papers are:
Poisson intensity estimation with reproducing kernels by Seth Flaxman, Yee Whye Teh, Dino Sejdinovic
Relativistic Monte Carlo by Xiaoyu Lu, Valerio Perrone, Leonard Hasenclever, Yee Whye Teh, Sebastian Vollmer
Encrypted accelerated least squares regression by Pedro Esperança, Louis Aslett, Chris Holmes
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables by Chris Maddison, Andriy Mnih, Yee Whye Teh
- François Caron
- Arnaud Doucet
- Robin Evans
- Chris Holmes
- Geoff Nicholls
- Jennifer Rogers
- Dino Sejdinovic
- Yee Whye Teh