Publications

2024 Approximately Equivariant Neural Processes

Ashman, M., Diaconu, C., Weller, A., Bruinsma, W. P., and Turner, R. E.

arXiv preprint: 2406.13488

2024 Noise-Aware Differentially Private Regression via Meta-Learning

Räisä, O., Markou, S., Ashman, M., Bruinsma, W. P., Tobaben, M., Honkela, A., and Turner, R. E.

arXiv preprint: 2406.08569

2024 Safe Exploration in Dose Finding Clinical Trials with Heterogeneous Participants

Chien, I., Bruinsma, W. P., Gonzalez, J., and Turner, R. E.

International Conference on Machine Learning (ICML), 41th

2024 Translation-Equivariant Transformer Neural Processes

Ashman, M., Diaconu, C., Kim, J., Sivaraya, J., Markou, S., Requeima, J., Bruinsma, W. P., and Turner, R. E.

International Conference on Machine Learning (ICML), 41th

2024 A Foundation Model for the Earth System

Bodnar, C., Bruinsma, W. P., Lucic, A., Stanley, M., Vaughan, A., Brandstetter, J., Garvan, J., Riechert, M., Weyn, J., Dong, H., Gupta, J. K., Tambiratnam, K., Archibald, A., Wuh, C., Heider, E., Welling, M., Turner, R. E., Perdikaris, P.

arXiv preprint: 2405.13063

2024 Aardvark Weather: End-to-End Data-Driven Weather Forecasting

Vaughan, A., Markou, S., Tebbutt, W., Requeima, J., Bruinsma, W. P., Andersson, T. R., Herzog, M., Lane, N. D., Chantry, M., Hosking, J. S., and Turner, R. E.

arXiv preprint: 2404.00411

2023 Autoregressive Conditional Neural Processes

Bruinsma, W. P., Markou, S., Requeima, J., Foong, A. Y. K., Andersson, T. R., Vaughan, A., Anthony, B., Hosking, J. S., and Turner, R. E.

International Conference on Representation Learning (ICLR), 11th

2023 Environmental Sensor Placement with Convolutional Gaussian Neural Processes

Andersson, T. R., Bruinsma, W. P., Markou, S., Requeima, J., Coca-Castro, A., Vaughan, A., Ellis, A.-L., Lazzara, M., Jones, D. C., Hosking, J. S., and Turner, R. E.

Environmental Data Science 2

2022 Sparse Gaussian Process Hyperparameters: Optimize or Integrate?

Lalchand, V., Bruinsma, W. P., Burt, D. R., and Rasmussen, C. E.

Advances in Neural Information Processing Systems (NeurIPS), 36th

2022 Challenges and Pitfalls of Bayesian Unlearning

Rawat, A., Requeima, J. R., Bruinsma, W. P., and Turner, R. E.

Updatable Machine Learning (UpML), ICML 2022 Workshop on

2022 A Note on the Chernoff Bound for Random Variables in the Unit Interval

Foong, A. Y. K., Bruinsma, W. P., and Burt, D.

arXiv:2205.07880

2022 Modelling Non-Smooth Signals with Complex Spectral Structure

Bruinsma, W. P., Tegnér, M., and Turner, R. E.

Artificial Intelligence and Statistics (AISTATS), 25th International Conference on

2022 Wide Mean-Field Bayesian Neural Networks Ignore the Data

Coker, B., Burt D., Bruinsma, W. P., Pan W., and Doshi–Velez, F.

Artificial Intelligence and Statistics (AISTATS), 25th International Conference on

2022 Practical Conditional Neural Processes Via Tractable Dependent Predictions

Markou, S., Requeima, J. R., Bruinsma, W. P., and Turner, R. E.

International Conference on Learning Representations (ICLR), 10th

2021 Efficient Gaussian Neural Processes for Regression

Markou, S., Requeima, J. R., Brunisma, W. P., and Turner, R. E.

Uncertainty & Robustness in Deep Learning (UDL), ICML 2021 Workshop on

2021 How Tight Can PAC-Bayes be in the Small Data Regime?

Foong, A. Y. K., Bruinsma W. P., Burt D. R., and Turner, R. E.

Advances in Neural Information Processing Systems (NeurIPS), 35th

2021 The Gaussian Neural Process contributed talk

Bruinsma, W. P., Requeima, J., Foong, A. Y. K., Gordon, J., and Turner, R. E.

Advances in Approximate Bayesian Inference (AABI), 3rd Symposium on

2021 The Gaussian Process Latent Autoregressive Model

Xia, R., Bruinsma, W. P., Tebbutt, W., and Turner, R. E.

Advances in Approximate Bayesian Inference (AABI), 3rd Symposium on

2020 Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes

Foong, A. Y. K., Bruinsma W. P., Gordon. J., Dubois, Y., Requeima, J., and Turner, R. E.

Advances in Neural Information Processing Systems (NeurIPS), 33th

2020 Scalable Exact Inference in Multi-Output Gaussian Processes

Bruinsma, W. P., Perim E., Tebbutt W., Hosking, J. S., Solin, A., and Turner, R. E.

International Conference on Machine Learning (ICML), 37th

2020 Convolutional Conditional Neural Processes oral presentation

Gordon, J., Bruinsma W. P., Foong, A. Y. K., Requeima, J., Dubois Y., and Turner, R. E.

International Conference on Learning Representations (ICLR), 8th

2019 GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process Models

Berkovich, B., Perim, E., and Bruinsma, W. P.

Advances in Approximate Bayesian Inference (AABI), 2nd Symposium on

2019 The Gaussian Process Autoregressive Model (GPAR)

Requeima, J., Tebbutt, W. C., Bruinsma, W. P., and Turner, R. E.

Artificial Intelligence and Statistics (AISTATS), 22nd International Conference on

2018 Learning Causally Generated Stationary Time Series

Bruinsma, W. P. and Turner, R. E.

arXiv:1802.08167

2017 Grating Lobe Prediction in 3D Array Antennas

Bosma, S., Bruinsma, W. P., Hes, R. P., Bentum, M. J., and Lager, I. E.

Antennas and Propagation (EuCAP), 11th European Conference on

2016 Radiation Properties of Moving Constellations of (Nano) Satellites: A Complexity Study

Bruinsma, W. P., Hes, R. P., Bosma, S., Lager, I. E., and Bentum, M. J.

Antennas and Propagation (EuCAP), 10th European Conference on

2015 Beamforming in Sparse, Random, 3D Array Antennas with Fluctuating Element Locations

Bentum, M. J., Lager, I. E., Bosma, S., Bruinsma, W. P., and Hes, R. P.

Antennas and Propagation (EuCAP), 9th European Conference on

2024 Foundation Models for Earth Systems

Bruinsma, W. P.

The Lorentz Center Workshop: Advancing Ecosystem Carbon Flux Research

2024 Autoregressive Conditional Neural Processes

Bruinsma, W. P.

Center for Basic Machine Learing in Life Sciences (MLLS), University of Copenhagen

2023 Autoregressive Conditional Neural Processes video

Bruinsma, W. P., Markou, S., Requeima, J., Foong, A. Y. K., Andersson, T. R., Vaughan, A., Anthony, B., Hosking, J. S., and Turner, R. E.

International Conference on Representation Learning (ICLR), 11th

2023 Autoregressive Conditional Neural Processes

Bruinsma, W. P., Markou, S., Requeima, J., Foong, A. Y. K., Andersson, T. R., Vaughan, A., Anthony, B., Hosking, J. S., and Turner, R. E.

International Conference on Representation Learning (ICLR), 11th

2023 Active Learning With Convolutional Gaussian Neural Processes For Environmental Sensor Placement video

Andersson, T. R., Bruinsma, W. P., Markou, S., Requeima, J., Coca-Castro, A., Vaughan, A., Ellis, A.-L., Lazzara, M., Jones, D. C., Hosking, J. S., and Turner, R. E.

Environmental Data Science (Climate Informatics 2023 Special Issue)

2023 Meta-Learning as Prediction Map Approximation

Bruinsma, W. P.

Sheffield Machine Learning Group, University of Sheffield

2021 Cover's Guessing Game

Bruinsma, W. P.

Computational and Biological Learning Lab, University of Cambridge

2020 The Gaussian Neural Process video

Bruinsma, W. P., Requeima, J., Foong, A. Y. K., Gordon, J., and Turner, R. E.

Advances in Approximate Bayesian Inference (AABI), 3rd Symposium on

2020 The Gaussian Neural Process

Bruinsma, W. P., Requeima J., Foong, A. Y. K., Gordon, J., and Turner, R. E.

Advances in Approximate Bayesian Inference (AABI), 3rd Symposium on

2020 On Sparse Variational Methods and the KL Between Stochastic Processes

Bruinsma, W. P.

Gaussian Process Reading Group

2020 Sequential Inference and Decision Making

Hron, J. and Bruinsma, W. P.

Computational and Biological Learning Lab, University of Cambridge

2020 NeuralProcesses.jl: Composing Neural Processes with Flux video

Bruinsma, W. P., Gordon, J., and Turner, R. E.

JuliaCon

2020 NeuralProcesses.jl: Composing Neural Processes with Flux

Bruinsma, W. P., Gordon, J., and Turner, R. E.

JuliaCon

2020 Scalable Exact Inference in Multi-Output Gaussian Processes video

Bruinsma, W. P., Perim, E., Tebbutt, W., Hosking, J. S., Solin, A., and Turner, R. E.

International Conference on Machine Learning (ICML), 37th

2020 Scalable Exact Inference in Multi-Output Gaussian Processes

Bruinsma, W. P., Perim E., Tebbutt, W., Hosking, J. S., Solin, A., and Turner, R. E.

International Conference on Machine Learning (ICML), 37th

2020 Compositional Model Design: High-Dimensional Multi-Output Regression

Bruinsma, W. P.

Computational and Biological Learning Lab, University of Cambridge

2020 Points and Circles

Bruinsma, W. P.

Computational and Biological Learning Lab, University of Cambridge

2019 Orthogonal Bases for Multi-Output Gaussian Processes

Bruinsma, W. P.

Computational and Biological Learning Lab, University of Cambridge

2019 Spectral Methods in Gaussian Modelling: Spectrum Estimation

Requeima, J. R. and Bruinsma, W. P.

Complex Systems Spectral Methods

2019 Spectral Methods in Gaussian Modelling: Variational Inference

Requeima, J. R. and Bruinsma, W. P.

Complex Systems Spectral Methods

2019 Spectral Methods in Gaussian Modelling: Kernel Design

Requeima, J. R. and Bruinsma, W. P.

Complex Systems Spectral Methods

2019 A Bayesian Truth Serum

Bruinsma, W. P.

Computational and Biological Learning Lab, University of Cambridge

2019 Agreeing to Disagree

Bruinsma, W. P.

InveniaCon

2019 The Gaussian Process Convolution Model

Bruinsma, W. P.

Computational and Biological Learning Lab, University of Cambridge

2017 Reasoning About the World

Bruinsma, W. P.

InveniaCon

2022 Convolutional Conditional Neural Processes

Bruinsma, W. P.

Department of Engineering, University of Cambridge. Thesis for the degree Doctor of Philosophy.

2016 The Generalised Gaussian Process Convolution Model

Bruinsma, W. P.

Department of Engineering, University of Cambridge. Thesis for the degree Master of Philosophy.

2015 An Extensible Toolkit for Real-Time High-Performance Wideband Spectrum Sensing

Bruinsma, W. P., Hes, R. P., Kroep, H. J. C., Leliveld, T. C., Melching, W. M., and aan de Wiel, T. A.

Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology. Thesis for the degree Bachelor of Science.

2023 Autoregressive Conditional Neural Processes

Bruinsma, W. P., Markou, S., Requeima, J., Foong, A. Y. K., Andersson, T. R., Vaughan, A., Anthony, B., Hosking, J. S., and Turner, R. E.

International Conference on Representation Learning (ICLR), 11th

2022 Modelling Non-Smooth Signals with Complex Spectral Structure

Bruinsma, W. P., Tegnér, M., and Turner, R. E.

Artificial Intelligence and Statistics (AISTATS), 25th International Conference on

2022 Wide Mean-Field Bayesian Neural Networks Ignore the Data

Coker, B., Burt, D., Bruinsma, W. P., Pan, W., and Doshi–Velez, F.

Artificial Intelligence and Statistics (AISTATS), 25th International Conference on

2022 Practical Conditional Neural Processes Via Tractable Dependent Predictions

Markou, S., Requeima, J. R., Bruinsma, W. P., and Turner, R. E.

International Conference on Learning Representations (ICLR), 10th

2021 How Tight Can PAC-Bayes be in the Small Data Regime?

Foong, A. Y. K., Bruinsma, W. P., Burt, D. R., and Turner, R. E.

Advances in Neural Information Processing Systems (NeurIPS), 35th

2020 Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes

Foong, A. Y. K., Bruinsma, W. P., Gordon, J., Dubois, Y., Requeima, J., and Turner, R. E.

Advances in Neural Information Processing Systems (NeurIPS), 33th

2019 The Gaussian Process Autoregressive Model (GPAR)

Requeima, J., Tebbutt, W. C., Bruinsma, W. P., and Turner, R. E.

Artificial Intelligence and Statistics (AISTATS), 22nd International Conference on

2018 Gaussian Process Probabilistic Programming

Tebbutt, W. C., Bruinsma, W. P., and Turner, R. E.

Probabilistic Programming (ProbProg), The International Conference on

2016 Learning with Nonparametric Kernels

Bruinsma, W. P., Tobar, F., and Turner, R. E.

Industry Session for MPhil in Machine Learning and Machine Intelligence

Spectral Methods in Gaussian Modelling

Requeima, J. R., and Bruinsma, W. P.

A Bayesian Truth Serum

Bruinsma, W. P.

Spike and Slab Priors

Bruinsma, W. P.

Agreeing to Disagree

Bruinsma, W. P.