Vincent Dutordoir

According to our database, Vincent Dutordoir authored at least 20 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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

DEFT: Efficient Finetuning of Conditional Diffusion Models by Learning the Generalised h-transform.

Alexander Denker, Francisco Vargas, Shreyas Padhy, Kieran Didi, Simon V. Mathis, Vincent Dutordoir, Riccardo Barbano, Emile Mathieu, Urszula Julia Komorowska, Pietro Lio

CoRR, 2024

2023

A framework for conditional diffusion modelling with applications in motif scaffolding for protein design.

Kieran Didi, Francisco Vargas, Simon V. Mathis, Vincent Dutordoir, Emile Mathieu, Urszula Julia Komorowska, Pietro Lio

CoRR, 2023

Geometric Neural Diffusion Processes.

Emile Mathieu, Vincent Dutordoir, Michael J. Hutchinson, Valentin De Bortoli, Yee Whye Teh, Richard E. Turner

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes.

Louis C. Tiao, Vincent Dutordoir, Victor Picheny

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the International Conference on Machine Learning, 2023

Neural Diffusion Processes.

Vincent Dutordoir, Alan Saul, Zoubin Ghahramani, Fergus Simpson

Proceedings of the International Conference on Machine Learning, 2023

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

Scalable Thompson Sampling using Sparse Gaussian Process Models.

Sattar Vakili, Henry B. Moss, Artem Artemev, Vincent Dutordoir, Victor Picheny

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

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

A Tutorial on Sparse Gaussian Processes and Variational Inference.

Felix Leibfried, Vincent Dutordoir, S. T. John, Nicolas Durrande

CoRR, 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

Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation.

Victor Picheny, Vincent Dutordoir, Artem Artemev, Nicolas Durrande

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

Sparse Gaussian Processes with Spherical Harmonic Features.

Vincent Dutordoir, Nicolas Durrande, James Hensman

Proceedings of the 37th International Conference on Machine Learning, 2020

Bayesian Image Classification with Deep Convolutional Gaussian Processes.

Vincent Dutordoir, Mark van der Wilk, Artem Artemev, James Hensman

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Translation Insensitivity for Deep Convolutional Gaussian Processes.

Vincent Dutordoir, Mark van der Wilk, Artem Artemev, Marcin Tomczak, James Hensman

CoRR, 2019

Deep Gaussian Processes with Importance-Weighted Variational Inference.

Hugh Salimbeni, Vincent Dutordoir, James Hensman, Marc Peter Deisenroth

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Gaussian Process Conditional Density Estimation.

Vincent Dutordoir, Hugh Salimbeni, James Hensman, Marc Peter Deisenroth

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Deep Gaussian Process metamodeling of sequentially sampled non-stationary response surfaces.

Vincent Dutordoir, Nicolas Knudde, Joachim van der Herten, Ivo Couckuyt, Tom Dhaene

Proceedings of the 2017 Winter Simulation Conference, 2017