Mark van der Wilk

According to our database, Mark van der Wilk authored at least 61 papers between 2014 and 2024.

Collaborative distances:

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

2024

Transfer Learning Bayesian Optimization to Design Competitor DNA Molecules for Use in Diagnostic Assays.

Ruby Sedgwick, John P. Goertz, Molly M. Stevens, Ruth Misener, Mark van der Wilk

CoRR, 2024

Recommendations for Baselines and Benchmarking Approximate Gaussian Processes.

Sebastian W. Ober, Artem Artemev, Marcel Wagenländer, Rudolfs Grobins, Mark van der Wilk

CoRR, 2024

Transition Constrained Bayesian Optimization via Markov Decision Processes.

Jose Pablo Folch, Calvin Tsay, Robert M. Lee, Behrang Shafei, Weronika Ormaniec, Andreas Krause, Mark van der Wilk, Ruth Misener, Mojmír Mutný

CoRR, 2024

2023

Combining multi-fidelity modelling and asynchronous batch Bayesian Optimization.

Jose Pablo Folch, Robert M. Lee, Behrang Shafei, David Walz, Calvin Tsay, Mark van der Wilk, Ruth Misener

Comput. Chem. Eng., April, 2023

Turbulence: Systematically and Automatically Testing Instruction-Tuned Large Language Models for Code.

Shahin Honarvar, Mark van der Wilk, Alastair Donaldson

CoRR, 2023

Practical Path-based Bayesian Optimization.

Jose Pablo Folch, James Odgers, Shiqiang Zhang, Robert M. Lee, Behrang Shafei, David Walz, Calvin Tsay, Mark van der Wilk, Ruth Misener

CoRR, 2023

Learning in Deep Factor Graphs with Gaussian Belief Propagation.

Seth Nabarro, Mark van der Wilk, Andrew J. Davison

CoRR, 2023

Current Methods for Drug Property Prediction in the Real World.

Jacob Green, Cecilia Cabrera Diaz, Maximilian A. H. Jakobs, Andrea Dimitracopoulos, Mark van der Wilk, Ryan D. Greenhalgh

CoRR, 2023

Causal Discovery using Bayesian Model Selection.

Anish Dhir, Mark van der Wilk

CoRR, 2023

Learning Layer-wise Equivariances Automatically using Gradients.

Tycho F. A. van der Ouderaa, Alexander Immer, Mark van der Wilk

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels.

Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk, Gunnar Rätsch, Bernhard Schölkopf

Proceedings of the International Conference on Machine Learning, 2023

Actually Sparse Variational Gaussian Processes.

Harry Jake Cunningham, Daniel Augusto de Souza, So Takao, Mark van der Wilk, Marc Peter Deisenroth

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees.

Alexander Terenin, David R. Burt, Artem Artemev, Seth R. Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge

CoRR, 2022

Memory Safe Computations with XLA Compiler.

Artem Artemev, Tilman Roeder, Mark van der Wilk

CoRR, 2022

Learning invariant weights in neural networks.

Tycho F. A. van der Ouderaa, Mark van der Wilk

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Data augmentation in Bayesian neural networks and the cold posterior effect.

Seth Nabarro, Stoil Ganev, Adrià Garriga-Alonso, Vincent Fortuin, Mark van der Wilk, Laurence Aitchison

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Relaxing Equivariance Constraints with Non-stationary Continuous Filters.

Tycho F. A. van der Ouderaa, David W. Romero, Mark van der Wilk

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

Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations.

Alexander Immer, Tycho F. A. van der Ouderaa, Gunnar Rätsch, Vincent Fortuin, Mark van der Wilk

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

SnAKe: Bayesian Optimization with Pathwise Exploration.

Jose Pablo Folch, Shiqiang Zhang, Robert M. Lee, Behrang Shafei, David Walz, Calvin Tsay, Mark van der Wilk, Ruth Misener

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

Memory safe computations with XLA compiler.

Artem Artemev, Yuze An, Tilman Roeder, Mark van der Wilk

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

Sparse Convolutions on Lie Groups.

Tycho F. A. van der Ouderaa, Mark van der Wilk

Proceedings of the NeurIPS Workshop on Symmetry and Geometry in Neural Representations, 2022

Bayesian Neural Network Priors Revisited.

Vincent Fortuin, Adrià Garriga-Alonso, Sebastian W. Ober, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison

Proceedings of the Tenth International Conference on Learning Representations, 2022

Last Layer Marginal Likelihood for Invariance Learning.

Pola Schwöbel, Martin Jørgensen, Sebastian W. Ober, Mark van der Wilk

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

2021

<i>BNNpriors</i>: A library for Bayesian neural network inference with different prior distributions.

Vincent Fortuin, Adrià Garriga-Alonso, Mark van der Wilk, Laurence Aitchison

Softw. Impacts, 2021

Barely Biased Learning for Gaussian Process Regression.

David R. Burt, Artem Artemev, Mark van der Wilk

CoRR, 2021

A Bayesian Approach to Invariant Deep Neural Networks.

Nikolaos Mourdoukoutas, Marco Federici, Georges Pantalos, Mark van der Wilk, Vincent Fortuin

CoRR, 2021

BNNpriors: A library for Bayesian neural network inference with different prior distributions.

Vincent Fortuin, Adrià Garriga-Alonso, Mark van der Wilk, Laurence Aitchison

CoRR, 2021

GPflux: A Library for Deep Gaussian Processes.

Vincent Dutordoir, Hugh Salimbeni, Eric Hambro, John McLeod, Felix Leibfried, Artem Artemev, Mark van der Wilk, James Hensman, Marc Peter Deisenroth, S. T. John

CoRR, 2021

Bayesian Neural Network Priors Revisited.

Vincent Fortuin, Adrià Garriga-Alonso, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison

CoRR, 2021

The promises and pitfalls of deep kernel learning.

Sebastian W. Ober, Carl E. Rasmussen, Mark van der Wilk

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Correlated weights in infinite limits of deep convolutional neural networks.

Adrià Garriga-Alonso, Mark van der Wilk

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Speedy Performance Estimation for Neural Architecture Search.

Robin Ru, Clare Lyle, Lisa Schut, Miroslav Fil, Mark van der Wilk, Yarin Gal

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

Deep Neural Networks as Point Estimates for Deep Gaussian Processes.

Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande

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

Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients.

Artem Artemev, David R. Burt, Mark van der Wilk

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Convergence of Sparse Variational Inference in Gaussian Processes Regression.

David R. Burt, Carl Edward Rasmussen, Mark van der Wilk

J. Mach. Learn. Res., 2020

Design of Experiments for Verifying Biomolecular Networks.

Ruby Sedgwick, John P. Goertz, Molly M. Stevens, Ruth Misener, Mark van der Wilk

CoRR, 2020

Understanding Variational Inference in Function-Space.

David R. Burt, Sebastian W. Ober, Adrià Garriga-Alonso, Mark van der Wilk

CoRR, 2020

A Bayesian Perspective on Training Speed and Model Selection.

Clare Lyle, Lisa Schut, Binxin Ru, Yarin Gal, Mark van der Wilk

CoRR, 2020

Variational Orthogonal Features.

David R. Burt, Carl Edward Rasmussen, Mark van der Wilk

CoRR, 2020

Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search.

Binxin Ru, Clare Lyle, Lisa Schut, Mark van der Wilk, Yarin Gal

CoRR, 2020

On the Benefits of Invariance in Neural Networks.

Clare Lyle, Mark van der Wilk, Marta Kwiatkowska, Yarin Gal, Benjamin Bloem-Reddy

CoRR, 2020

Capsule Networks - A Probabilistic Perspective.

Lewis Smith, Lisa Schut, Yarin Gal, Mark van der Wilk

CoRR, 2020

A Framework for Interdomain and Multioutput Gaussian Processes.

Mark van der Wilk, Vincent Dutordoir, S. T. John, Artem Artemev, Vincent Adam, James Hensman

CoRR, 2020

Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty.

Miguel Monteiro, Loïc Le Folgoc, Daniel Coelho de Castro, Nick Pawlowski, Bernardo Marques, Konstantinos Kamnitsas, Mark van der Wilk, Ben Glocker

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

A Bayesian Perspective on Training Speed and Model Selection.

Clare Lyle, Lisa Schut, Robin Ru, Yarin Gal, Mark van der Wilk

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

Bayesian Image Classification with Deep Convolutional Gaussian Processes.

Vincent Dutordoir, Mark van der Wilk, Artem Artemev, James Hensman

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Sparse Gaussian process approximations and applications.

Mark van der Wilk

PhD thesis, 2019

Translation Insensitivity for Deep Convolutional Gaussian Processes.

Vincent Dutordoir, Mark van der Wilk, Artem Artemev, Marcin Tomczak, James Hensman

CoRR, 2019

Bayesian Layers: A Module for Neural Network Uncertainty.

Dustin Tran, Mike Dusenberry, Mark van der Wilk, Danijar Hafner

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes.

Creighton Heaukulani, Mark van der Wilk

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models.

Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen

Proceedings of the 36th International Conference on Machine Learning, 2019

Rates of Convergence for Sparse Variational Gaussian Process Regression.

David R. Burt, Carl Edward Rasmussen, Mark van der Wilk

Proceedings of the 36th International Conference on Machine Learning, 2019

Variational Gaussian Process Models without Matrix Inverses.

Mark van der Wilk, S. T. John, Artem Artemev, James Hensman

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

2018

Non-Factorised Variational Inference in Dynamical Systems.

Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen

CoRR, 2018

Closed-form Inference and Prediction in Gaussian Process State-Space Models.

Alessandro Davide Ialongo, Mark van der Wilk, Carl Edward Rasmussen

CoRR, 2018

Learning Invariances using the Marginal Likelihood.

Mark van der Wilk, Matthias Bauer, S. T. John, James Hensman

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

GPflow: A Gaussian Process Library using TensorFlow.

Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo León-Villagrá, Zoubin Ghahramani, James Hensman

J. Mach. Learn. Res., 2017

Convolutional Gaussian Processes.

Mark van der Wilk, Carl Edward Rasmussen, James Hensman

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning.

Rowan McAllister, Yarin Gal, Alex Kendall, Mark van der Wilk, Amar Shah, Roberto Cipolla, Adrian Weller

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

2016

Understanding Probabilistic Sparse Gaussian Process Approximations.

Matthias Bauer, Mark van der Wilk, Carl Edward Rasmussen

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2014

Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models.

Yarin Gal, Mark van der Wilk, Carl E. Rasmussen

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014