Vincent Dutordoir

32 papers A* 10A 1Misc 1Journal 19Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and Graphs. J. Mach. Learn. Res. Peter Mostowsky, Vincent Dutordoir, Iskander Azangulov, Noémie Jaquier, Michael John Hutchinson, Aditya Ravuri, Leonel Rozo, Alexander Terenin, Viacheslav Borovitskiy
2024	A*	`conf`	DEFT: Efficient Fine-tuning of Diffusion Models by Learning the Generalised $h$-transform. NeurIPS Alexander Denker, Francisco Vargas, Shreyas Padhy, Kieran Didi, Simon V. Mathis, Riccardo Barbano, Vincent Dutordoir, Emile Mathieu, Urszula Julia Komorowska, Pietro Lió
2024	J	`jnl`	DEFT: Efficient Finetuning of Conditional Diffusion Models by Learning the Generalised h-transform. CoRR Alexander Denker, Francisco Vargas, Shreyas Padhy, Kieran Didi, Simon V. Mathis, Vincent Dutordoir, Riccardo Barbano, Emile Mathieu, Urszula Julia Komorowska, Pietro Lio
2024	J	`jnl`	The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and Graphs. CoRR Peter Mostowsky, Vincent Dutordoir, Iskander Azangulov, Noémie Jaquier, Michael John Hutchinson, Aditya Ravuri, Leonel Rozo, Alexander Terenin, Viacheslav Borovitskiy
2023	J	`jnl`	A framework for conditional diffusion modelling with applications in motif scaffolding for protein design. CoRR Kieran Didi, Francisco Vargas, Simon V. Mathis, Vincent Dutordoir, Emile Mathieu, Urszula Julia Komorowska, Pietro Lio
2023	A*	`conf`	Geometric Neural Diffusion Processes. NeurIPS Emile Mathieu, Vincent Dutordoir, Michael J. Hutchinson, Valentin De Bortoli, Yee Whye Teh, Richard E. Turner
2023	J	`jnl`	Geometric Neural Diffusion Processes. CoRR Emile Mathieu, Vincent Dutordoir, Michael J. Hutchinson, Valentin De Bortoli, Yee Whye Teh, Richard E. Turner
2023	A*	`conf`	Memory-Based Meta-Learning on Non-Stationary Distributions. ICML Tim Genewein, Grégoire Delétang, Anian Ruoss, Li Kevin Wenliang, Elliot Catt, Vincent Dutordoir, Jordi Grau-Moya, Laurent Orseau, Marcus Hutter, Joel Veness
2023	J	`jnl`	Memory-Based Meta-Learning on Non-Stationary Distributions. CoRR Tim Genewein, Grégoire Delétang, Anian Ruoss, Li Kevin Wenliang, Elliot Catt, Vincent Dutordoir, Jordi Grau-Moya, Laurent Orseau, Marcus Hutter, Joel Veness
2023	A*	`conf`	Neural Diffusion Processes. ICML Vincent Dutordoir, Alan Saul, Zoubin Ghahramani, Fergus Simpson
2023	A*	`conf`	Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes. ICML Louis C. Tiao, Vincent Dutordoir, Victor Picheny
2023	J	`jnl`	Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes. CoRR Louis C. Tiao, Vincent Dutordoir, Victor Picheny
2022	J	`jnl`	Neural Diffusion Processes. CoRR Vincent Dutordoir, Alan Saul, Zoubin Ghahramani, Fergus Simpson
2021	A*	`conf`	Deep Neural Networks as Point Estimates for Deep Gaussian Processes. NeurIPS Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande
2021	J	`jnl`	Deep Neural Networks as Point Estimates for Deep Gaussian Processes. CoRR Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande
2021	J	`jnl`	GPflux: A Library for Deep Gaussian Processes. CoRR Vincent Dutordoir, Hugh Salimbeni, Eric Hambro, John McLeod, Felix Leibfried, Artem Artemev, Mark van der Wilk, James Hensman, Marc Peter Deisenroth, S. T. John
2021	A*	`conf`	Scalable Thompson Sampling using Sparse Gaussian Process Models. NeurIPS Sattar Vakili, Henry B. Moss, Artem Artemev, Vincent Dutordoir, Victor Picheny
2020	J	`jnl`	A Framework for Interdomain and Multioutput Gaussian Processes. CoRR Mark van der Wilk, Vincent Dutordoir, S. T. John, Artem Artemev, Vincent Adam, James Hensman
2020	J	`jnl`	A Tutorial on Sparse Gaussian Processes and Variational Inference. CoRR Felix Leibfried, Vincent Dutordoir, S. T. John, Nicolas Durrande
2020	—	`conf`	Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation. ECML/PKDD (3) Victor Picheny, Vincent Dutordoir, Artem Artemev, Nicolas Durrande
2020	J	`jnl`	Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation. CoRR Victor Picheny, Vincent Dutordoir, Artem Artemev, Nicolas Durrande
2020	A	`conf`	Bayesian Image Classification with Deep Convolutional Gaussian Processes. AISTATS Vincent Dutordoir, Mark van der Wilk, Artem Artemev, James Hensman
2020	J	`jnl`	Hierarchical Gaussian Process Models for Improved Metamodeling. ACM Trans. Model. Comput. Simul. Nicolas Knudde, Vincent Dutordoir, Joachim van der Herten, Ivo Couckuyt, Tom Dhaene
2020	J	`jnl`	Scalable Thompson Sampling using Sparse Gaussian Process Models. CoRR Sattar Vakili, Henry B. Moss, Victor Picheny, Vincent Dutordoir, Artem Artemev
2020	A*	`conf`	Sparse Gaussian Processes with Spherical Harmonic Features. ICML Vincent Dutordoir, Nicolas Durrande, James Hensman
2020	J	`jnl`	Sparse Gaussian Processes with Spherical Harmonic Features. CoRR Vincent Dutordoir, Nicolas Durrande, James Hensman
2019	A*	`conf`	Deep Gaussian Processes with Importance-Weighted Variational Inference. ICML Hugh Salimbeni, Vincent Dutordoir, James Hensman, Marc Peter Deisenroth
2019	J	`jnl`	Deep Gaussian Processes with Importance-Weighted Variational Inference. CoRR Hugh Salimbeni, Vincent Dutordoir, James Hensman, Marc Peter Deisenroth
2019	J	`jnl`	Translation Insensitivity for Deep Convolutional Gaussian Processes. CoRR Vincent Dutordoir, Mark van der Wilk, Artem Artemev, Marcin Tomczak, James Hensman
2018	A*	`conf`	Gaussian Process Conditional Density Estimation. NeurIPS Vincent Dutordoir, Hugh Salimbeni, James Hensman, Marc Peter Deisenroth
2018	J	`jnl`	Gaussian Process Conditional Density Estimation. CoRR Vincent Dutordoir, Hugh Salimbeni, Marc Peter Deisenroth, James Hensman
2017	Misc	`conf`	Deep Gaussian Process metamodeling of sequentially sampled non-stationary response surfaces. WSC Vincent Dutordoir, Nicolas Knudde, Joachim van der Herten, Ivo Couckuyt, Tom Dhaene