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Wessel Bruinsma
Wessel Bruinsma
Microsoft Research Amsterdam
Verified email at microsoft.com - Homepage
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Cited by
Year
Convolutional Conditional Neural Processes
J Gordon, WP Bruinsma, AYK Foong, J Requeima, Y Dubois, RE Turner
International Conference on Learning Representations (ICLR), 8th, 2020
1412020
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
562020
The Gaussian Process Autoregressive Regression Model (GPAR)
J Requeima, W Tebbutt, W Bruinsma, RE Turner
Artificial Intelligence and Statistics (AISTATS), 22nd International …, 2019
442019
The Gaussian Neural Process
WP Bruinsma, J Requeima, AYK Foong, J Gordon, RE Turner
Advances in Approximate Bayesian Inference (AABI), 3rd Symposium on, 2021
322021
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
282020
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
252021
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
202022
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
182022
Autoregressive Conditional Neural Processes
WP Bruinsma, S Markou, J Requiema, AYK Foong, TR Andersson, ...
International Conference on Learning Representations (ICLR), 11th, 2023
102023
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
102021
Modelling Non-Smooth Signals with Complex Spectral Structure
WP Bruinsma, M Tegnér, RE Turner
International Conference on Artificial Intelligence and Statistics, 5166-5195, 2022
92022
Sparse Gaussian Process Hyperparameters: Optimize or Integrate?
V Lalchand, WP Bruinsma, DR Burt, CE Rasmussen
Advances in Neural Information Processing Systems (NeurIPS), 36th, 2022
42022
Challenges and Pitfalls of Bayesian Unlearning
A Rawat, J Requeima, W Bruinsma, R Turner
Updatable Machine Learning (UpML), ICML 2022 Workshop on, 2022
42022
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
42022
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
32023
The Gaussian Process Latent Autoregressive Model
R Xia, W Bruinsma, W Tebbutt, RE Turner
Advances in Approximate Bayesian Inference (AABI), 3rd Symposium on., 2020
32020
The Generalised Gaussian Process Convolution Model
W Bruinsma
University of Cambridge, 2016
32016
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
32015
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
12023
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
12020
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