About us
The Fundamentals of Statistical Machine Learning group is a research group in the UCL Department of Statistical Science. Our focus is on the intersection of statistical inference and machine learning methodology and theory.
Meet the Team
Faculty
François-Xavier Briol
Associate Professor
Jeremias Knoblauch
Assistant Professor
EPSRC Fellow
Postdoctoral Researchers
Ieva Kazlauskaitė
IMSS Fellow
PhD Students
Matias Altamirano
PhD Student
Zonghao (Hudson) Chen
PhD Student
Oscar Key
PhD Student
Yann McLatchie
PhD Student
Masha Naslidnyk
PhD Student
Iman Simo-Dzumgang
PhD Student
Ilina Yozova
PhD Student
Visiting Researchers
Xing Liu
PhD Student
Saifuddin Syed
Florence Nightingale Bicentennial Fellow
Alumni
Ayush Bharti
Visitor, Autumn 2022
Kaiyu Li
PhD, 2019-2024
Zhuo Sun
PhD, 2019-2023
Veit Wild
Visitor, Autumn 2023
News
Many members of the group attend and present at the ‘Functional inference and machine intelligence’ workshop at the University of Bristol.
Iman Simo-Dzumgang joins the group as a PhD student co-supervised by Dr. F-X Briol and Dr. Jeremias Knoblauch.
Kaiyu Li is awarded the James Nelson Prize for her paper on ‘Multilevel Bayesian quadrature’. This award is for the best paper published during the preceding year by a research student registered in the Department of Statistical Science at UCL.
Zhuo Sun and Ilina Yozova are awarded the MAPS Faculty Research Excellence and Teaching Excellence Awards respectively.
Dr. Jeremias Knoblauch spends a month on a research visit at Monash University, where he is hosted by Prof. David Frazier.
Recent Publications
(2024).
A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods.
In NeurIPS.
(2023).
Multilevel Bayesian Quadrature.
In AISTATS.
(2023).
No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models.
In NeurIPS.
(2023).
Robust and Conjugate Gaussian Process Regression.
In arXiv.
(2023).
Robustifying likelihoods by optimistically re-weighting data.
In arXiv.
(2023).
Vector-Valued Control Variates.
In ICML.
(2023).
Comparing Scale Parameter Estimators for Gaussian Process Regression: Cross Validation and Maximum Likelihood.
In arXiv.
(2023).
Optimally-Weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference.
In ICML.
(2023).
Robust and Scalable Bayesian Online Changepoint Detection.
In ICML.
Major Research Funding
EPSRC New Investigator Award. Project on ‘Transfer Learning for Monte Carlo Methods’ (PI: Briol)
EPSRC Small Grant in the Mathematical Sciences, Project on “Robust Foundations for Bayesian Inference” (PI: Briol, co-I: Knoblauch)
UKRI/Turing Project “Digital Twin Handbook” (co-I: Briol)
EP/W005859/1: EPSRC Fellowship, Project on “Optimisation-centric Generalisations of Bayesian Inference” (PI: Knoblauch)
Biometrika fellowship: “Generalising Bayesian Inference” (PI: Knoblauch)
Amazon Research Award: Project on “Transfer Learning for Numerical Integration in Expensive Machine Learning Systems” (PI: Briol)
Contact
- 1-19 torrington place, London, WC1E 7HB