Presentations

23 lectures, tutorials and invited talks, 2007–2025. Slides are not currently hosted.

2025
Bayesian Optimization and Foundation Models
Machine Learning module of the StatML Centre for Doctoral Training, Oxford
2025
2024
Explaining Probabilistic Models with Distributional Values
ELLIS Robust ML workshop, Helsinki
2024
Bayesian Optimization and A Primer on Foundation Models
Machine Learning module of the StatML Centre for Doctoral Training, Oxford
2024
2022
Open (Practical) Problems in Machine Learning Automation
Department of Statistics, University of Oxford
2022
Algorithms for Automated Hyperparameter and Neural Architecture Optimization
Machine Learning module of the StatML Centre for Doctoral Training, Oxford
2022
2021
Algorithms for Automated Hyperparameter and Neural Architecture Optimization and Variational Inference
Machine Learning module of the StatML Centre for Doctoral Training, Oxford
2021
Bayesian Optimization by Density-Ratio Estimation
Mini-symposium on Bayesian Methods in Science and Engineering at the SIAM Conference on Computational Science and Engineering
2021
2020
Automated HP and Architecture Tuning. (recording)
CVPR 2020 tutorial From HPO to NAS: Automated Deep Learning
2020
[Slides]
2019
Learning Representations to Accelerate Hyperparameter Tuning
Computational Statistics and Machine Learning Seminars, Oxford
2019
Bayesian Optimisation and Variational Inference
Machine Learning module of the OxWaSP Centre for Doctoral Training, Oxford
2019
2018
L'Apprentissage Statistique et son Application en Industrie
Congrès MATh.en.Jeans, Potsdam
2018
Learning Representations for Hyperparameter Transfer Learning
DALI 2018 workshop on Goals and Principles of Representation Learning, Lanzarote
2018
Bayesian Optimisation and Variational Inference
Machine Learning module of the OxWaSP Centre for Doctoral Training, Oxford
2018
2017
A Playground for Machine Learning
Data Science Summer School (DS3), Paris, 2017: Tutorial on Bayesian Optimisation; Amazon
2017
Bayesian Optimisation
Machine Learning Tutorial at Imperial College, London
2017
Two Applications at Amazon
NeurIPS workshop on Advances in Approximate Bayesian Inference (AABI), Long Beach, 2017: Approximate Bayesian Inference in Industry
2017
2016
Bayesian Optimisation
Machine Learning Summer School (MLSS 2016, Arequipa)
2016
[Slides]
Classification and Clustering
Peyresq Summer School in Signal and Image Processing '16
2016
2012
Statistical Principles and Methods
Engineering in Computer Science '12 at ENSIMAG
2012
2011
Machine Learning at Xerox -- From statistical machine translation to large-scale image search
MSc in Machine Learning '11 (Applied Machine Learning) at UCL
2011
2010
Tutorial on Probabilistic Graphical Models at PASCAL Bootcamp
2010
[Video]
2008
Advanced Topics in Machine Learning
MSc in Intelligent Systems '08 at UCL
2008
2007
CSML'07 reading group on Stochastic Differential Equations.
2007