| Convolutional Conditional Neural Processes J Gordon, WP Bruinsma, AYK Foong, J Requeima, Y Dubois, RE Turner International Conference on Learning Representations (ICLR), 8th, 2020 | 133 | 2020 |
| Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes AYK Foong, WP Bruinsma, J Gordon, Y Dubois, J Requeima, RE Turner Advances in Neural Information Processing Systems (NeurIPS), 33th, 2020 | 53 | 2020 |
| The Gaussian Process Autoregressive Regression Model (GPAR) J Requeima, W Tebbutt, W Bruinsma, RE Turner Artificial Intelligence and Statistics (AISTATS), 22nd International …, 2019 | 41 | 2019 |
| The Gaussian Neural Process WP Bruinsma, J Requeima, AYK Foong, J Gordon, RE Turner Advances in Approximate Bayesian Inference (AABI), 3rd Symposium on, 2021 | 31 | 2021 |
| Scalable Exact Inference in Multi-Output Gaussian Processes W Bruinsma, E Perim, W Tebbutt, S Hosking, A Solin, R Turner International Conference on Machine Learning (ICML), 37th, 2020 | 26 | 2020 |
| How Tight Can PAC-Bayes be in the Small Data Regime? AYK Foong, WP Bruinsma, DR Burt, RE Turner Advances in Neural Information Processing Systems (NeurIPS), 35th, 2021 | 21 | 2021 |
| Practical Conditional Neural Process Via Tractable Dependent Predictions S Markou, J Requeima, W Bruinsma, A Vaughan, RE Turner International Conference on Learning Representations (ICLR), 10th, 2022 | 17 | 2022 |
| Wide Mean-Field Bayesian Neural Networks Ignore the Data B Coker, WP Bruinsma, DR Burt, W Pan, F Doshi-Velez Artificial Intelligence and Statistics (AISTATS), 25th International …, 2022 | 17 | 2022 |
| Modelling Non-Smooth Signals with Complex Spectral Structure WP Bruinsma, M Tegnér, RE Turner International Conference on Artificial Intelligence and Statistics, 5166-5195, 2022 | 9 | 2022 |
| Autoregressive Conditional Neural Processes WP Bruinsma, S Markou, J Requiema, AYK Foong, TR Andersson, ... International Conference on Learning Representations (ICLR), 11th, 2023 | 8 | 2023 |
| Efficient Gaussian Neural Processes for Regression S Markou, J Requeima, W Bruinsma, R Turner Uncertainty & Robustness in Deep Learning (UDL), ICML 2021 Workshop on, 2021 | 8 | 2021 |
| A Note on the Chernoff Bound for Random Variables in the Unit Interval AYK Foong, WP Bruinsma, DR Burt arXiv preprint arXiv:2205.07880, 2022 | 4 | 2022 |
| Sparse Gaussian Process Hyperparameters: Optimize or Integrate? V Lalchand, WP Bruinsma, DR Burt, CE Rasmussen Advances in Neural Information Processing Systems (NeurIPS), 36th, 2022 | 3 | 2022 |
| The Gaussian Process Latent Autoregressive Model R Xia, W Bruinsma, W Tebbutt, RE Turner Advances in Approximate Bayesian Inference (AABI), 3rd Symposium on., 2020 | 3 | 2020 |
| The Generalised Gaussian Process Convolution Model W Bruinsma University of Cambridge, 2016 | 3 | 2016 |
| Beamforming in Sparse, Random, 3D Array Antennas with Fluctuating Element Locations MJ Bentum, IE Lager, S Bosma, WP Bruinsma, RP Hes Antennas and Propagation (EuCAP), 9th European Conference on, 2015 | 3 | 2015 |
| Active Learning with Convolutional Gaussian Neural Processes for Environmental Sensor Placement TR Andersson, WP Bruinsma, S Markou, DC Jones, JS Hosking, ... Environmental Data Science (Climate Informatics 2023 Special Issue), 2023 | 2 | 2023 |
| Environmental sensor placement with convolutional Gaussian neural processes TR Andersson, WP Bruinsma, S Markou, J Requeima, A Coca-Castro, ... Environmental Data Science 2, e32, 2023 | 1 | 2023 |
| Challenges and Pitfalls of Bayesian Unlearning A Rawat, J Requeima, W Bruinsma, R Turner Updatable Machine Learning (UpML), ICML 2022 Workshop on, 2022 | 1 | 2022 |
| GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process Models P Berkovich, E Perim, W Bruinsma Advanced in Approximate Bayesian Inference (AABI), 2nd Sympo- sium on, 2020 | 1 | 2020 |
Richard E TurnerProfessor, University of CambridgeVerified email at cam.ac.uk
