| 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 | 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 | 51 | 2018 |
| Sparse Gaussian processes with spherical harmonic features V Dutordoir, N Durrande, J Hensman International Conference on Machine Learning, 2793-2802, 2020 | 43 | 2020 |
| Deep Gaussian Processes with Importance-Weighted Variational Inference H Salimbeni, V Dutordoir, J Hensman, MP Deisenroth International Conference on Machine Learning, ICML, 2019 | 43 | 2019 |
| 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 | 33* | 2020 |
| 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 | 30 | 2021 |
| A tutorial on sparse Gaussian processes and variational inference F Leibfried, V Dutordoir, ST John, N Durrande arXiv preprint arXiv:2012.13962, 2020 | 27 | 2020 |
| 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 | 19 | 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 | 17 | 2021 |
| Neural diffusion processes V Dutordoir, A Saul, Z Ghahramani, F Simpson arXiv preprint arXiv:2206.03992, 2022 | 7 | 2022 |
| 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 |
| 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 | 4 | 2020 |
| Efficient computational inference using gaussian processes V Dutordoir, J Hensman, N Durrande US Patent 11,027,743, 2021 | 1 | 2021 |
| 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 | 1 | 2021 |
| Method and system for classification of data J Hensman, M VAN DER WILK, V Dutordoir US Patent 10,733,483, 2020 | 1 | 2020 |
| Non-Stationary Surrogate Modeling with Deep Gaussian.” V Dutordoir | 1 | 2017 |
| Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes LC Tiao, V Dutordoir, V Picheny arXiv preprint arXiv:2304.14034, 2023 | | 2023 |
| Memory-Based Meta-Learning on Non-Stationary Distributions T Genewein, G Delétang, A Ruoss, LK Wenliang, E Catt, V Dutordoir, ... arXiv preprint arXiv:2302.03067, 2023 | | 2023 |
| Efficient computational inference using gaussian processes V Dutordoir, J Hensman, N Durrande US Patent App. 17/338,158, 2021 | | 2021 |
| Surrogate modeling with sequential design for design and analysis of electronic systems J van der Herten, V Dutordoir, I Couckuyt, T Dhaene 2017 International Conference on Electromagnetics in Advanced Applications …, 2017 | | 2017 |
James HensmanMicrosoft ResearchVerified email at microsoft.com
