Publications
Papers
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2023 Autoregressive Conditional Neural ProcessesBruinsma, 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
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2023 Active Learning With Convolutional Gaussian Neural Processes For Environmental Sensor PlacementAndersson, 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)
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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
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2022 Challenges and Pitfalls of Bayesian UnlearningRawat, A., Requeima, J. R., Bruinsma, W. P., and Turner, R. E.
Updatable Machine Learning (UpML), ICML 2022 Workshop on
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2022 A Note on the Chernoff Bound for Random Variables in the Unit IntervalFoong, A. Y. K., Bruinsma, W. P., and Burt, D.
arXiv:2205.07880
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2022 Modelling Non-Smooth Signals with Complex Spectral StructureBruinsma, W. P., Tegnér, M., and Turner, R. E.
Artificial Intelligence and Statistics (AISTATS), 25th International Conference on
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2022 Wide Mean-Field Bayesian Neural Networks Ignore the DataCoker, B., Burt D., Bruinsma, W. P., Pan W., and Doshi–Velez, F.
Artificial Intelligence and Statistics (AISTATS), 25th International Conference on
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2022 Practical Conditional Neural Processes Via Tractable Dependent PredictionsMarkou, S., Requeima, J. R., Bruinsma, W. P., and Turner, R. E.
International Conference on Learning Representations (ICLR), 10th
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2021 Efficient Gaussian Neural Processes for RegressionMarkou, S., Requeima, J. R., Brunisma, W. P., and Turner, R. E.
Uncertainty & Robustness in Deep Learning (UDL), ICML 2021 Workshop on
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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
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2021 The Gaussian Neural Process contributed talkBruinsma, W. P., Requeima, J., Foong, A. Y. K., Gordon, J., and Turner, R. E.
Advances in Approximate Bayesian Inference (AABI), 3rd Symposium on
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2021 The Gaussian Process Latent Autoregressive ModelXia, R., Bruinsma, W. P., Tebbutt, W., and Turner, R. E.
Advances in Approximate Bayesian Inference (AABI), 3rd Symposium on
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2020 Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural ProcessesFoong, A. Y. K., Bruinsma W. P., Gordon. J., Dubois, Y., Requeima, J., and Turner, R. E.
Advances in Neural Information Processing Systems (NeurIPS), 33th
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2020 Scalable Exact Inference in Multi-Output Gaussian ProcessesBruinsma, W. P., Perim E., Tebbutt W., Hosking, J. S., Solin, A., and Turner, R. E.
International Conference on Machine Learning (ICML), 37th
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2020 Convolutional Conditional Neural Processes oral presentationGordon, J., Bruinsma W. P., Foong, A. Y. K., Requeima, J., Dubois Y., and Turner, R. E.
International Conference on Learning Representations (ICLR), 8th
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2019 GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process ModelsBerkovich, B., Perim, E., and Bruinsma, W. P.
Advances in Approximate Bayesian Inference (AABI), 2nd Symposium on
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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
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2018 Learning Causally Generated Stationary Time SeriesBruinsma, W. P. and Turner, R. E.
arXiv:1802.08167
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2017 Grating Lobe Prediction in 3D Array AntennasBosma, S., Bruinsma, W. P., Hes, R. P., Bentum, M. J., and Lager, I. E.
Antennas and Propagation (EuCAP), 11th European Conference on
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2016 Radiation Properties of Moving Constellations of (Nano) Satellites: A Complexity StudyBruinsma, W. P., Hes, R. P., Bosma, S., Lager, I. E., and Bentum, M. J.
Antennas and Propagation (EuCAP), 10th European Conference on
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2015 Beamforming in Sparse, Random, 3D Array Antennas with Fluctuating Element LocationsBentum, M. J., Lager, I. E., Bosma, S., Bruinsma, W. P., and Hes, R. P.
Antennas and Propagation (EuCAP), 9th European Conference on
Talks
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2023 Autoregressive Conditional Neural ProcessesBruinsma, W. P.
Center for Basic Machine Learing in Life Sciences (MLLS), University of Copenhagen
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2023 Autoregressive Conditional Neural Processes videoBruinsma, 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
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2023 Autoregressive Conditional Neural ProcessesBruinsma, 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
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2023 Active Learning With Convolutional Gaussian Neural Processes For Environmental Sensor Placement videoAndersson, 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)
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2023 Meta-Learning as Prediction Map ApproximationBruinsma, W. P.
Sheffield Machine Learning Group, University of Sheffield
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2021 Cover's Guessing GameBruinsma, W. P.
Computational and Biological Learning Lab, University of Cambridge
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2020 The Gaussian Neural Process videoBruinsma, W. P., Requeima, J., Foong, A. Y. K., Gordon, J., and Turner, R. E.
Advances in Approximate Bayesian Inference (AABI), 3rd Symposium on
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2020 The Gaussian Neural ProcessBruinsma, W. P., Requeima J., Foong, A. Y. K., Gordon, J., and Turner, R. E.
Advances in Approximate Bayesian Inference (AABI), 3rd Symposium on
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2020 On Sparse Variational Methods and the KL Between Stochastic ProcessesBruinsma, W. P.
Gaussian Process Reading Group
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2020 Sequential Inference and Decision MakingHron, J. and Bruinsma, W. P.
Computational and Biological Learning Lab, University of Cambridge
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2020 NeuralProcesses.jl: Composing Neural Processes with Flux videoBruinsma, W. P., Gordon, J., and Turner, R. E.
JuliaCon
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2020 NeuralProcesses.jl: Composing Neural Processes with FluxBruinsma, W. P., Gordon, J., and Turner, R. E.
JuliaCon
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2020 Scalable Exact Inference in Multi-Output Gaussian Processes videoBruinsma, W. P., Perim, E., Tebbutt, W., Hosking, J. S., Solin, A., and Turner, R. E.
International Conference on Machine Learning (ICML), 37th
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2020 Scalable Exact Inference in Multi-Output Gaussian ProcessesBruinsma, W. P., Perim E., Tebbutt, W., Hosking, J. S., Solin, A., and Turner, R. E.
International Conference on Machine Learning (ICML), 37th
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2020 Compositional Model Design: High-Dimensional Multi-Output RegressionBruinsma, W. P.
Computational and Biological Learning Lab, University of Cambridge
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2020 Points and CirclesBruinsma, W. P.
Computational and Biological Learning Lab, University of Cambridge
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2019 Orthogonal Bases for Multi-Output Gaussian ProcessesBruinsma, W. P.
Computational and Biological Learning Lab, University of Cambridge
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2019 Spectral Methods in Gaussian Modelling: Spectrum EstimationRequeima, J. R. and Bruinsma, W. P.
Complex Systems Spectral Methods
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2019 Spectral Methods in Gaussian Modelling: Variational InferenceRequeima, J. R. and Bruinsma, W. P.
Complex Systems Spectral Methods
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2019 Spectral Methods in Gaussian Modelling: Kernel DesignRequeima, J. R. and Bruinsma, W. P.
Complex Systems Spectral Methods
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2019 A Bayesian Truth SerumBruinsma, W. P.
Computational and Biological Learning Lab, University of Cambridge
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2019 Agreeing to DisagreeBruinsma, W. P.
InveniaCon
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2019 The Gaussian Process Convolution ModelBruinsma, W. P.
Computational and Biological Learning Lab, University of Cambridge
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2017 Reasoning About the WorldBruinsma, W. P.
InveniaCon
Theses
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2016 The Generalised Gaussian Process Convolution ModelBruinsma, W. P.
Department of Engineering, University of Cambridge. Thesis for the degree Master of Philosophy.
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2015 An Extensible Toolkit for Real-Time High-Performance Wideband Spectrum SensingBruinsma, 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.
Posters
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2023 Autoregressive Conditional Neural ProcessesBruinsma, 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
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2022 Modelling Non-Smooth Signals with Complex Spectral StructureBruinsma, W. P., Tegnér, M., and Turner, R. E.
Artificial Intelligence and Statistics (AISTATS), 25th International Conference on
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2022 Wide Mean-Field Bayesian Neural Networks Ignore the DataCoker, B., Burt, D., Bruinsma, W. P., Pan, W., and Doshi–Velez, F.
Artificial Intelligence and Statistics (AISTATS), 25th International Conference on
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2022 Practical Conditional Neural Processes Via Tractable Dependent PredictionsMarkou, S., Requeima, J. R., Bruinsma, W. P., and Turner, R. E.
International Conference on Learning Representations (ICLR), 10th
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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
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2020 Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural ProcessesFoong, A. Y. K., Bruinsma, W. P., Gordon, J., Dubois, Y., Requeima, J., and Turner, R. E.
Advances in Neural Information Processing Systems (NeurIPS), 33th
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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
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2018 Gaussian Process Probabilistic ProgrammingTebbutt, W. C., Bruinsma, W. P., and Turner, R. E.
Probabilistic Programming (ProbProg), The International Conference on
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2016 Learning with Nonparametric KernelsBruinsma, W. P., Tobar, F., and Turner, R. E.
Industry Session for MPhil in Machine Learning and Machine Intelligence