Deep Gaussian Process metamodeling of sequentially sampled non-stationary response surfaces. - Vincent Dutordoir; Nicolas Knudde; Joachim van der Herten; Ivo Couckuyt; Tom Dhaene (WSC, 2017)

Gaussian Process Conditional Density Estimation. - Vincent Dutordoir; Hugh Salimbeni; James Hensman; Marc Peter Deisenroth (NeurIPS, 2018)

Deep Gaussian Processes with Importance-Weighted Variational Inference. - Hugh Salimbeni; Vincent Dutordoir; James Hensman; Marc Peter Deisenroth (ICML, 2019)

Translation Insensitivity for Deep Convolutional Gaussian Processes. - Vincent Dutordoir; Mark van der Wilk; Artem Artemev; Marcin Tomczak; James Hensman (CoRR, 2019)

Bayesian Image Classification with Deep Convolutional Gaussian Processes. - Vincent Dutordoir; Mark van der Wilk; Artem Artemev; James Hensman (AISTATS, 2020)

Sparse Gaussian Processes with Spherical Harmonic Features. - Vincent Dutordoir; Nicolas Durrande; James Hensman (ICML, 2020)

Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation. - Victor Picheny; Vincent Dutordoir; Artem Artemev; Nicolas Durrande (ECML/PKDD (3), 2020)

A Framework for Interdomain and Multioutput Gaussian Processes. - Mark van der Wilk; Vincent Dutordoir; S. T. John; Artem Artemev; Vincent Adam; James Hensman (CoRR, 2020)

A Tutorial on Sparse Gaussian Processes and Variational Inference. - Felix Leibfried; Vincent Dutordoir; S. T. John; Nicolas Durrande (CoRR, 2020)

Hierarchical Gaussian Process Models for Improved Metamodeling. - Nicolas Knudde; Vincent Dutordoir; Joachim van der Herten; Ivo Couckuyt; Tom Dhaene (ACM Trans. Model. Comput. Simul., 2020)

Deep Neural Networks as Point Estimates for Deep Gaussian Processes. - Vincent Dutordoir; James Hensman; Mark van der Wilk; Carl Henrik Ek; Zoubin Ghahramani; Nicolas Durrande (NeurIPS, 2021)

Scalable Thompson Sampling using Sparse Gaussian Process Models. - Sattar Vakili; Henry B. Moss; Artem Artemev; Vincent Dutordoir; Victor Picheny (NeurIPS, 2021)

GPflux: A Library for Deep Gaussian Processes. - Vincent Dutordoir; Hugh Salimbeni; Eric Hambro; John McLeod; Felix Leibfried; Artem Artemev; Mark van der Wilk; James Hensman; Marc Peter Deisenroth; S. T. John (CoRR, 2021)

Neural Diffusion Processes. - Vincent Dutordoir; Alan Saul; Zoubin Ghahramani; Fergus Simpson (ICML, 2023)

Memory-Based Meta-Learning on Non-Stationary Distributions. - Tim Genewein; Grégoire Delétang; Anian Ruoss; Li Kevin Wenliang; Elliot Catt; Vincent Dutordoir; Jordi Grau-Moya; Laurent Orseau; Marcus Hutter; Joel Veness (ICML, 2023)

Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes. - Louis C. Tiao; Vincent Dutordoir; Victor Picheny (ICML, 2023)

Geometric Neural Diffusion Processes. - Emile Mathieu; Vincent Dutordoir; Michael J. Hutchinson; Valentin De Bortoli; Yee Whye Teh; Richard E. Turner (NeurIPS, 2023)

A framework for conditional diffusion modelling with applications in motif scaffolding for protein design. - Kieran Didi; Francisco Vargas 001; Simon V. Mathis; Vincent Dutordoir; Emile Mathieu; Urszula Julia Komorowska; Pietro Lio (CoRR, 2023)

DEFT: Efficient Fine-tuning of Diffusion Models by Learning the Generalised $h$-transform. - Alexander Denker; Francisco Vargas 001; Shreyas Padhy; Kieran Didi; Simon V. Mathis; Riccardo Barbano; Vincent Dutordoir; Emile Mathieu; Urszula Julia Komorowska; Pietro Lió (NeurIPS, 2024)

DEFT: Efficient Finetuning of Conditional Diffusion Models by Learning the Generalised h-transform. - Alexander Denker; Francisco Vargas 001; Shreyas Padhy; Kieran Didi; Simon V. Mathis; Vincent Dutordoir; Riccardo Barbano; Emile Mathieu; Urszula Julia Komorowska; Pietro Lio (CoRR, 2024)

The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and Graphs. - Peter Mostowsky; Vincent Dutordoir; Iskander Azangulov; Noémie Jaquier; Michael John Hutchinson; Aditya Ravuri; Leonel Rozo; Alexander Terenin; Viacheslav Borovitskiy (CoRR, 2024)