Mark van der Wilk

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Papers (12)

1. Meta-learning for sample-efficient Bayesian optimisation of fed-batch processes math.OC · 2026 · author #8
2. Symmetry Guarantees Statistic Recovery in Variational Inference stat.ML · 2026 · author #3
3. Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models stat.ML · 2019 · author #2
4. Non-Factorised Variational Inference in Dynamical Systems stat.ML · 2018 · author #2
5. Bayesian Layers: A Module for Neural Network Uncertainty cs.LG · 2018 · author #3
6. Closed-form Inference and Prediction in Gaussian Process State-Space Models stat.ML · 2018 · author #2
7. Learning Invariances using the Marginal Likelihood cs.LG · 2018 · author #1
8. Convolutional Gaussian Processes stat.ML · 2017 · author #1
9. GPflow: A Gaussian process library using TensorFlow stat.ML · 2016 · author #2
10. Understanding Probabilistic Sparse Gaussian Process Approximations stat.ML · 2016 · author #2
11. Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models - a Gentle Tutorial stat.ML · 2014 · author #2
12. Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models stat.ML · 2014 · author #2

Mentions

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Frequent Coauthors

• Carl Edward Rasmussen 5 shared papers
• James Hensman 5 shared papers
• Alessandro Davide Ialongo 3 shared papers
• Matthias Bauer 2 shared papers
• Yarin Gal 2 shared papers
• Alexander G. de G. Matthews 1 shared papers
• Alexis Boukouvalas 1 shared papers
• Becky Langdon 1 shared papers
• Behrang Shafei 1 shared papers
• Calvin Tsay 1 shared papers
• Carl E. Rasmussen 1 shared papers
• Chrysoula D. Kappatou 1 shared papers
• Daniel Marks 1 shared papers
• Danijar Hafner 1 shared papers
• Dario Paccagnan 1 shared papers
• Dustin Tran 1 shared papers
• Gabriel D. Patr\'on 1 shared papers
• Jixiang Qing 1 shared papers
• Keisuke Fujii 1 shared papers
• Michael W. Dusenberry 1 shared papers