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Vincent Dutordoir
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2020 – today
- 2024
- [i16]Alexander Denker, Francisco Vargas, Shreyas Padhy, Kieran Didi, Simon V. Mathis, Vincent Dutordoir, Riccardo Barbano, Emile Mathieu, Urszula Julia Komorowska, Pietro Lio:
DEFT: Efficient Finetuning of Conditional Diffusion Models by Learning the Generalised h-transform. CoRR abs/2406.01781 (2024) - [i15]Peter Mostowsky, Vincent Dutordoir, Iskander Azangulov, Noémie Jaquier, Michael John Hutchinson, Aditya Ravuri, Leonel Rozo, Alexander Terenin, Viacheslav Borovitskiy:
The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and Graphs. CoRR abs/2407.08086 (2024) - 2023
- [c12]Vincent Dutordoir, Alan Saul, Zoubin Ghahramani, Fergus Simpson:
Neural Diffusion Processes. ICML 2023: 8990-9012 - [c11]Tim Genewein, Grégoire Delétang, Anian Ruoss, Li Kevin Wenliang, Elliot Catt, Vincent Dutordoir, Jordi Grau-Moya, Laurent Orseau, Marcus Hutter, Joel Veness:
Memory-Based Meta-Learning on Non-Stationary Distributions. ICML 2023: 11173-11195 - [c10]Louis C. Tiao, Vincent Dutordoir, Victor Picheny:
Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes. ICML 2023: 34143-34160 - [c9]Emile Mathieu, Vincent Dutordoir, Michael J. Hutchinson, Valentin De Bortoli, Yee Whye Teh, Richard E. Turner:
Geometric Neural Diffusion Processes. NeurIPS 2023 - [i14]Tim Genewein, Grégoire Delétang, Anian Ruoss, Li Kevin Wenliang, Elliot Catt, Vincent Dutordoir, Jordi Grau-Moya, Laurent Orseau, Marcus Hutter, Joel Veness:
Memory-Based Meta-Learning on Non-Stationary Distributions. CoRR abs/2302.03067 (2023) - [i13]Louis C. Tiao, Vincent Dutordoir, Victor Picheny:
Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes. CoRR abs/2304.14034 (2023) - [i12]Emile Mathieu, Vincent Dutordoir, Michael J. Hutchinson, Valentin De Bortoli, Yee Whye Teh, Richard E. Turner:
Geometric Neural Diffusion Processes. CoRR abs/2307.05431 (2023) - [i11]Kieran Didi, Francisco Vargas, Simon V. Mathis, Vincent Dutordoir, Emile Mathieu, Urszula Julia Komorowska, Pietro Lio:
A framework for conditional diffusion modelling with applications in motif scaffolding for protein design. CoRR abs/2312.09236 (2023) - 2022
- [i10]Vincent Dutordoir, Alan Saul, Zoubin Ghahramani, Fergus Simpson:
Neural Diffusion Processes. CoRR abs/2206.03992 (2022) - 2021
- [c8]Sattar Vakili, Henry B. Moss, Artem Artemev, Vincent Dutordoir, Victor Picheny:
Scalable Thompson Sampling using Sparse Gaussian Process Models. NeurIPS 2021: 5631-5643 - [c7]Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande:
Deep Neural Networks as Point Estimates for Deep Gaussian Processes. NeurIPS 2021: 9443-9455 - [i9]Vincent Dutordoir, Hugh Salimbeni, Eric Hambro, John McLeod, Felix Leibfried, Artem Artemev, Mark van der Wilk, James Hensman, Marc Peter Deisenroth, S. T. John:
GPflux: A Library for Deep Gaussian Processes. CoRR abs/2104.05674 (2021) - [i8]Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande:
Deep Neural Networks as Point Estimates for Deep Gaussian Processes. CoRR abs/2105.04504 (2021) - 2020
- [j1]Nicolas Knudde
, Vincent Dutordoir, Joachim van der Herten, Ivo Couckuyt, Tom Dhaene:
Hierarchical Gaussian Process Models for Improved Metamodeling. ACM Trans. Model. Comput. Simul. 30(4): 23:1-23:17 (2020) - [c6]Vincent Dutordoir, Mark van der Wilk, Artem Artemev, James Hensman:
Bayesian Image Classification with Deep Convolutional Gaussian Processes. AISTATS 2020: 1529-1539 - [c5]Vincent Dutordoir, Nicolas Durrande, James Hensman:
Sparse Gaussian Processes with Spherical Harmonic Features. ICML 2020: 2793-2802 - [c4]Victor Picheny, Vincent Dutordoir, Artem Artemev, Nicolas Durrande:
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation. ECML/PKDD (3) 2020: 431-446 - [i7]Mark van der Wilk, Vincent Dutordoir, S. T. John, Artem Artemev, Vincent Adam, James Hensman:
A Framework for Interdomain and Multioutput Gaussian Processes. CoRR abs/2003.01115 (2020) - [i6]Victor Picheny, Vincent Dutordoir, Artem Artemev, Nicolas Durrande:
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation. CoRR abs/2006.14376 (2020) - [i5]Vincent Dutordoir, Nicolas Durrande, James Hensman:
Sparse Gaussian Processes with Spherical Harmonic Features. CoRR abs/2006.16649 (2020) - [i4]Felix Leibfried, Vincent Dutordoir, S. T. John, Nicolas Durrande:
A Tutorial on Sparse Gaussian Processes and Variational Inference. CoRR abs/2012.13962 (2020)
2010 – 2019
- 2019
- [c3]Hugh Salimbeni, Vincent Dutordoir, James Hensman, Marc Peter Deisenroth:
Deep Gaussian Processes with Importance-Weighted Variational Inference. ICML 2019: 5589-5598 - [i3]Vincent Dutordoir, Mark van der Wilk, Artem Artemev, Marcin Tomczak, James Hensman:
Translation Insensitivity for Deep Convolutional Gaussian Processes. CoRR abs/1902.05888 (2019) - [i2]Hugh Salimbeni, Vincent Dutordoir, James Hensman, Marc Peter Deisenroth:
Deep Gaussian Processes with Importance-Weighted Variational Inference. CoRR abs/1905.05435 (2019) - 2018
- [c2]Vincent Dutordoir, Hugh Salimbeni, James Hensman, Marc Peter Deisenroth:
Gaussian Process Conditional Density Estimation. NeurIPS 2018: 2391-2401 - [i1]Vincent Dutordoir, Hugh Salimbeni, Marc Peter Deisenroth, James Hensman:
Gaussian Process Conditional Density Estimation. CoRR abs/1810.12750 (2018) - 2017
- [c1]Vincent Dutordoir, Nicolas Knudde
Deep Gaussian Process metamodeling of sequentially sampled non-stationary response surfaces. WSC 2017: 1728-1739
Coauthor Index
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