
Computational Statistics

Machine Learning

Statistical Methodology
Computational Statistics
Machine Learning
Statistical Methodology
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
Statistical Machine Learning, Bayesian methods, Bayesian nonparametrics, Statistical Network Analysis
Arnaud Doucet
Computational Statistics, Monte Carlo methods
Chris Holmes
Decision theory, biostatistics and precision medicine, probabilistic learning under model misspecification
Patrick Rebeschini
Distributed machine learning
Dino Sejdinovic
Statistical machine learning, kernel methods, nonparametric statistics
Yee Whye Teh
Bayesian nonparametrics, probabilistic learning, deep learning
Postdocs
Marco Battiston
Bayesian nonparametrics
Benjamin BloemReddy
Bayesian nonparametrics, probabilistic modeling and inference
Yunlong Jiao
Machine Learning, Kernel Methods, Sparsity Regularization, Computational Genomics
Juho Lee
Bayesian nonparametric models, random graphs
Konstantina Palla
nonparametric Bayesian methods and models
Tom Rainforth
Bayesian inference, probabilistic programming, Monte Carlo methods
ChiehHsi (Jessie) Wu
Computational statistics, machine learning, stratified medicine.
Graduate Students
Moustafa Abdalla
Multiview Learning, Timeseries modelling, Statistical Genetics
Anthony Caterini
HighDimensional Statistics, Monte Carlo Methods, Applied Mathematics
Sam Davenport
Gaussian Processes, fMRI data, Resampling methods, Random Field Theory
Giuseppe Di Benedetto
Bayesian nonparametrics, Machine Learning
Adam Foster
Probabilistic inference, probabilistic programming, Bayesian nonparametrics
Frauke Harms
Bayesian nonparametrics, machine learning, stochastic geometry
Leonard Hasenclever
Large scale machine learning, probabilistic inference, deep learning
Hyunjik Kim
Gaussian Processes, probabilistic inference, deep generative models
Adam R. Kosiorek
Approximate Inference, Deep Generative Models
Ho Chung Leon Law
Kernel methods, machine learning
Thibaut Lienart
Inference on graphical models, expectation propagation, particle methods
Xiaoyu Lu
Machine learning, Gaussian Process, Bayesian Optimization, Adaptive Importance Sampling
Simon Lyddon
Bayesian statistics, decision theory, computational statistics, machine learning.
Chris J. Maddison
Probabilistic inference, Monte Carlo methods, neural networks, point processes
Kaspar Märtens
Statistical machine learning, probabilistic inference, Gaussian Processes, multiview learning
Emile Mathieu
Bayesian nonparametrics, probabilistic inference, deep learning
Xenia Miscouridou
Machine Learning, Deep Generative Models, Bayesian nonparametrics, Networks
Jovana Mitrovic
Kernel methods, deep learning
Valerio Perrone
Bayesian nonparametrics, deep learning
Emilia Pompe
MCMC methods, Bayesian statistics
Dominic Richards
Optimisation, Monte Carlo Methods
Sebastian Schmon
Probabilistic inference, Computational Statistics, Markov chain Monte Carlo, HighDimensional Statistics
Stefan Webb
deep learning, deep generative models, probabilistic inference
Matthew Willetts
Large scale machine learning, Bayesian methods, VAEs, Time series segmentation and classification
Qinyi Zhang
Statistical Machine Learning, Kernel Method, Nonparametric Association Measures