Wessel P. Bruinsma

According to our database, Wessel P. Bruinsma authored at least 17 papers between 2019 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Autoregressive Conditional Neural Processes.

Wessel P. Bruinsma, Stratis Markou, James Requeima, Andrew Y. K. Foong, Tom R. Andersson, Anna Vaughan, Anthony Buonomo, J. Scott Hosking, Richard E. Turner

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Active Learning with Convolutional Gaussian Neural Processes for Environmental Sensor Placement.

Tom R. Andersson, Wessel P. Bruinsma, Stratis Markou, James Requeima, Alejandro Coca-Castro, Anna Vaughan, Anna-Louise Ellis, Matthew Lazzara, Daniel C. Jones, J. Scott Hosking, Richard E. Turner

CoRR, 2022

Challenges and Pitfalls of Bayesian Unlearning.

Ambrish Rawat, James Requeima, Wessel P. Bruinsma, Richard E. Turner

CoRR, 2022

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

Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt

CoRR, 2022

Practical Conditional Neural Processes Via Tractable Dependent Predictions.

Stratis Markou, James Requeima, Wessel P. Bruinsma, Anna Vaughan, Richard E. Turner

CoRR, 2022

Sparse Gaussian Process Hyperparameters: Optimize or Integrate?

Vidhi Lalchand, Wessel P. Bruinsma, David R. Burt, Carl Edward Rasmussen

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Practical Conditional Neural Process Via Tractable Dependent Predictions.

Stratis Markou, James Requeima, Wessel P. Bruinsma, Anna Vaughan, Richard E. Turner

Proceedings of the Tenth International Conference on Learning Representations, 2022

Wide Mean-Field Bayesian Neural Networks Ignore the Data.

Beau Coker, Wessel P. Bruinsma, David R. Burt, Weiwei Pan, Finale Doshi-Velez

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Modelling Non-Smooth Signals with Complex Spectral Structure.

Wessel P. Bruinsma, Martin Tegner, Richard E. Turner

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Efficient Gaussian Neural Processes for Regression.

Stratis Markou, James Requeima, Wessel P. Bruinsma, Richard E. Turner

CoRR, 2021

The Gaussian Neural Process.

Wessel P. Bruinsma, James Requeima, Andrew Y. K. Foong, Jonathan Gordon, Richard E. Turner

CoRR, 2021

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

Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt, Richard E. Turner

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

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

Andrew Y. K. Foong, Wessel P. Bruinsma, Jonathan Gordon, Yann Dubois, James Requeima, Richard E. Turner

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Scalable Exact Inference in Multi-Output Gaussian Processes.

Wessel P. Bruinsma, Eric Perim, William Tebbutt, J. Scott Hosking, Arno Solin, Richard E. Turner

Proceedings of the 37th International Conference on Machine Learning, 2020

Convolutional Conditional Neural Processes.

Jonathan Gordon, Wessel P. Bruinsma, Andrew Y. K. Foong, James Requeima, Yann Dubois, Richard E. Turner

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

The Gaussian Process Autoregressive Regression Model (GPAR).

James Requeima, William Tebbutt, Wessel P. Bruinsma, Richard E. Turner

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

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

Pavel Berkovich, Eric Perim, Wessel P. Bruinsma

Proceedings of the Symposium on Advances in Approximate Bayesian Inference, 2019