Presentations

40+ invited talks, tutorials and lectures. Slides are not currently hosted.

Invited talks

Explaining Probabilistic Models with Distributional Values
ELLIS Robust ML workshop, Helsinki
2024
Open (Practical) Problems in Machine Learning Automation
Department of Statistics, University of Oxford
2022
Bayesian Optimisation by Density-Ratio Estimation
Mini-symposium on Bayesian Methods in Science and Engineering at the SIAM Conference on Computational Science and Engineering
2021
Learning Representations to Accelerate Hyperparameter Tuning
Computational Statistics and Machine Learning Seminars, Oxford
2019
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
Two Examples of Learning with Partial Feedback at Amazon
Cracow University of Economics
2018
Amazon: A Playground for Machine Learning
Data Science Summer School (DS3), École Normale Supérieure, Paris
2017
Approximate Bayesian Inference in Industry: Two Applications at Amazon
NeurIPS workshop on Advances in Approximate Bayesian Inference (AABI), Long Beach
2017
Bayesian Optimisation with Tree-structured Dependencies
University of Edinburgh
2017
Bringing Powerful Artificial Intelligence to All Developers
FutureStack, Berlin
2017
Leveraging Massive Data Sets in Retail, Web Services and Devices
Journée du Labex Bézout on Data Science and Massive Data Analysis, Paris
2014
Latent IBP Compound Dirichlet Allocation: Sparse Topic Models Fit for Natural Languages
University of Washington, Seattle
2013
Multi-Task Learning: A Bayesian Approach
Columbia University, New York
2011
Machine Learning at Xerox: From statistical machine translation to large-scale image search
MSc in Machine Learning (Applied Machine Learning), University College London
2011
Variational Inference for Diffusion Processes
University of Cambridge
2008
Probabilistic Component Analysis and Extensions
Google, Zurich
2008
Robust Bayesian Clustering
IDIAP research institute, Martigny
2006

Tutorials

Automated HP and Architecture Tuning
Tutorial at the Conference on Computer Vision and Pattern Recognition (CVPR): From HPO to NAS: Automated Deep Learning
2020
[Slides]
Tutorial on Bayesian Optimisation
Data Science Summer School (DS3), École Normale Supérieure, Paris
2017
Bayesian Optimisation
Machine Learning Tutorial at Imperial College, London
2017
Tutorial on Bayesian Optimisation
Machine Learning Summer School (MLSS), Arequipa
2016
Classification and Clustering
Peyresq Summer School in Signal and Image Processing
2016
Tutorial on Probabilistic Graphical Models
PASCAL 2 Machine Learning Bootcamp, Marseille and Kofi Annan Centre of Excellence in ICT
2010
[Video]

Lectures

Bayesian Optimisation
Machine Learning module of the StatML Centre for Doctoral Training, Oxford
2025, 2024
Foundation Models
Machine Learning module of the StatML Centre for Doctoral Training, Oxford
2025
A Primer on Foundation Models
Machine Learning module of the StatML Centre for Doctoral Training, Oxford
2024
Algorithms for Automated Hyperparameter and Neural Architecture Optimisation
Machine Learning module of the StatML Centre for Doctoral Training, Oxford
2022, 2021
Variational Inference
Machine Learning module of the StatML Centre for Doctoral Training, Oxford
2021
Bayesian Optimisation
Machine Learning module of the OxWaSP Centre for Doctoral Training, Oxford
2019, 2018
Variational Inference
Machine Learning module of the OxWaSP Centre for Doctoral Training, Oxford
2019, 2018
Statistical Principles and Methods
Engineering in Computer Science, École nationale supérieure d'informatique et de mathématiques appliquées de Grenoble (ENSIMAG)
2012
Advanced Topics in Machine Learning
MSc in Intelligent Systems, University College London
2008
Artificial Neural Networks
MSc in Electrical Engineering, Université catholique de Louvain
2003–2005