A framework for interdomain and multioutput Gaussian processes M Van der Wilk, V Dutordoir, ST John, A Artemev, V Adam, J Hensman
arXiv preprint arXiv:2003.01115, 2020
111 2020 Sparse Gaussian processes with spherical harmonic features V Dutordoir, N Durrande, J Hensman
International Conference on Machine Learning, 2793-2802, 2020
77 2020 Gaussian process conditional density estimation V Dutordoir, H Salimbeni, J Hensman, M Deisenroth
Advances in Neural Information Processing Systems, NeurIPS, 2385-2395, 2018
70 2018 Deep Gaussian Processes with Importance-Weighted Variational Inference H Salimbeni, V Dutordoir, J Hensman, MP Deisenroth
International Conference on Machine Learning, ICML, 2019
59 2019 A tutorial on sparse Gaussian processes and variational inference F Leibfried, V Dutordoir, ST John, N Durrande
arXiv preprint arXiv:2012.13962, 2020
55 2020 Bayesian image classification with deep convolutional Gaussian processes V Dutordoir, M Wilk, A Artemev, J Hensman
International Conference on Artificial Intelligence and Statistics, 1529-1539, 2020
48 * 2020 Neural diffusion processes V Dutordoir, A Saul, Z Ghahramani, F Simpson
International Conference on Machine Learning, 8990-9012, 2023
45 2023 Scalable Thompson sampling using sparse Gaussian process models S Vakili, H Moss, A Artemev, V Dutordoir, V Picheny
Advances in neural information processing systems 34, 5631-5643, 2021
42 2021 Deep neural networks as point estimates for deep Gaussian processes V Dutordoir, J Hensman, M van der Wilk, CH Ek, Z Ghahramani, ...
Advances in Neural Information Processing Systems 34, 9443-9455, 2021
39 2021 GPflux: A library for deep Gaussian processes V Dutordoir, H Salimbeni, E Hambro, J McLeod, F Leibfried, A Artemev, ...
arXiv preprint arXiv:2104.05674, 2021
31 2021 A framework for conditional diffusion modelling with applications in motif scaffolding for protein design K Didi, F Vargas, SV Mathis, V Dutordoir, E Mathieu, UJ Komorowska, ...
arXiv preprint arXiv:2312.09236, 2023
10 2023 Memory-based meta-learning on non-stationary distributions T Genewein, G Delétang, A Ruoss, LK Wenliang, E Catt, V Dutordoir, ...
International conference on machine learning, 11173-11195, 2023
9 2023 Geometric neural diffusion processes E Mathieu, V Dutordoir, M Hutchinson, V De Bortoli, YW Teh, R Turner
Advances in Neural Information Processing Systems 36, 2024
7 2024 Deep gaussian process metamodeling of sequentially sampled non-stationary response surfaces V Dutordoir, N Knudde, J van der Herten, I Couckuyt, T Dhaene
Winter Simulation Conference, 134, 2017
7 2017 DEFT: Efficient Finetuning of Conditional Diffusion Models by Learning the Generalised A Denker, F Vargas, S Padhy, K Didi, S Mathis, V Dutordoir, R Barbano, ...
arXiv preprint arXiv:2406.01781, 2024
5 2024 Hierarchical gaussian process models for improved metamodeling N Knudde, V Dutordoir, JVD Herten, I Couckuyt, T Dhaene
ACM Transactions on Modeling and Computer Simulation (TOMACS) 30 (4), 1-17, 2020
5 2020 A tutorial on sparse Gaussian processes and variational inference. arXiv 2020 F Leibfried, V Dutordoir, S John, N Durrande
arXiv preprint arXiv:2012.13962, 0
5 Method and system for classification of data J Hensman, M VAN DER WILK, V Dutordoir
US Patent 10,733,483, 2020
3 2020 Automatic tuning of stochastic gradient descent with Bayesian optimisation V Picheny, V Dutordoir, A Artemev, N Durrande
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2021
2 2021 The GeometricKernels Package: Heat and Mat\'ern Kernels for Geometric Learning on Manifolds, Meshes, and Graphs P Mostowsky, V Dutordoir, I Azangulov, N Jaquier, MJ Hutchinson, ...
arXiv preprint arXiv:2407.08086, 2024
1 2024 
James HensmanMicrosoft ResearchDirección de correo verificada de microsoft.com
