The Oxford statistical machine learning group is engaged in developing machine learning techniques for analysing data that are scalable, flexible and robust. The group has particular strengths in Bayesian and probabilistic methods, kernel methods and deep learning, with applications to network analysis, recommender systems, text processing, spatio-temporal modelling, genetics and genomics.

The group in numbers:

  • NIPS papers: 48
  • NIPS orals: 7
  • ICML papers: 16
  • UAI papers: 16
  • AISTATS papers: 13
  • JMLR papers: 8

Faculty

François Caron

François Caron

Statistical Machine Learning, Bayesian methods, Bayesian nonparametrics, Statistical Network Analysis

Chris Holmes

Chris Holmes

Decision theory, biostatistics and precision medicine, probabilistic learning under model misspecification

Dino Sejdinovic

Dino Sejdinovic

Statistical machine learning, kernel methods, nonparametric statistics

Yee Whye Teh

Yee Whye Teh

Bayesian nonparametrics, probabilistic learning, deep learning

Post-docs

Yunlong Jiao

Yunlong Jiao

Machine Learning, Kernel Methods, Sparsity Regularization, Computational Genomics

Juho Lee

Juho Lee

Bayesian nonparametric models, random graphs

Tom Rainforth

Tom Rainforth

Bayesian inference, probabilistic programming, Monte Carlo methods

Graduate Students

Moustafa Abdalla

Moustafa Abdalla

Multi-view Learning, Time-series modelling, Statistical Genetics

Anthony Caterini

Anthony Caterini

High-Dimensional Statistics, Monte Carlo Methods, Applied Mathematics

Sam Davenport

Sam Davenport

Gaussian Processes, fMRI data, Resampling methods, Random Field Theory

Adam Foster

Adam Foster

Probabilistic inference, probabilistic programming, Bayesian nonparametrics

Frauke Harms

Frauke Harms

Bayesian nonparametrics, machine learning, stochastic geometry

Hyunjik Kim

Hyunjik Kim

Gaussian Processes, probabilistic inference, deep generative models

Thibaut Lienart

Thibaut Lienart

Inference on graphical models, expectation propagation, particle methods

Xiaoyu Lu

Xiaoyu Lu

Machine learning, Gaussian Process, Bayesian Optimization, Adaptive Importance Sampling

Simon Lyddon

Simon Lyddon

Bayesian statistics, decision theory, computational statistics, machine learning.

Chris J. Maddison

Chris J. Maddison

Probabilistic inference, Monte Carlo methods, neural networks, point processes

Kaspar Märtens

Kaspar Märtens

Statistical machine learning, probabilistic inference, Gaussian Processes, multi-view learning

Emile Mathieu

Emile Mathieu

Bayesian nonparametrics, probabilistic inference, deep learning

Xenia Miscouridou

Xenia Miscouridou

Machine Learning, Deep Generative Models, Bayesian nonparametrics, Networks

Sebastian Schmon

Sebastian Schmon

Probabilistic inference, Computational Statistics, Markov chain Monte Carlo, High-Dimensional Statistics

Stefan Webb

Stefan Webb

deep learning, deep generative models, probabilistic inference

Matthew Willetts

Matthew Willetts

Large scale machine learning, Bayesian methods, VAEs, Time series segmentation and classification

Qinyi Zhang

Qinyi Zhang

Statistical Machine Learning, Kernel Method, Nonparametric Association Measures

Alumni