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.
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
In collaboration with Oxford Sparks, machine learning group members Seth Flaxman, Hyunjik Kim, and Prof Yee Whye Teh created a two minute animation answering the question, “What is machine learning?”.
Many group members will be at NIPS 2016 presenting work at the main conference and workshops.
- Tamara Fernández will be presenting “Gaussian Processes for Survival Analysis” at the main conference.
- Stefan Webb will be presenting “A Tighter Monte Carlo Objective with Renyi alpha-Divergence Measures” at the Bayesian Deep Learning workshop.
- Hyunjik Kim will be presenting “Scalable Structure Discovery in Regression using Gaussian Processes” at the Practical Bayesian Nonparametrics workshop.
- Leonard Hasenclaver, Stefan Webb and Thibaut Lienart will be presenting “Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server” at the Advances in Approximate Bayesian Inference and Bayesian Deep Learning workshops.
- Valerio Perrone and Xiaoyu Lu will be presenting “Relativistic Monte Carlo” at the Bayesian Deep Learning workshop.
- Konstantina Palla will be presenting “Bayesian nonparametrics for Sparse Dynamic Networks”, Xiaoyu Lu will be presenting “Tucker Gaussian Process for Regression and Collaborative Filtering”, Qinyi Zhang will be presenting “Large-Scale Kernel Methods for Independence Testing” and Jovana Mitrovic will be presenting “Disentangling the Factors of Variation at Initialization In Neural Networks” at the Women in Machine Learning Workshop.