Mark van der Wilk

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 82

Top Topics

Gaussian Process

Top Venues

Publications

Jose Pablo Folch, Calvin Tsay, Robert M. Lee, Behrang Shafei, Weronika Ormaniec, Andreas Krause, Mark van der Wilk, Ruth Misener, Mojmír Mutný
Transition Constrained Bayesian Optimization via Markov Decision Processes. CoRR (2024)
Sebastian W. Ober, Artem Artemev, Marcel Wagenländer, Rudolfs Grobins, Mark van der Wilk
Recommendations for Baselines and Benchmarking Approximate Gaussian Processes. CoRR (2024)
Ruby Sedgwick, John P. Goertz, Molly M. Stevens, Ruth Misener, Mark van der Wilk
Transfer Learning Bayesian Optimization to Design Competitor DNA Molecules for Use in Diagnostic Assays. CoRR (2024)
Jixiang Qing, Becky D. Langdon, Robert M. Lee, Behrang Shafei, Mark van der Wilk, Calvin Tsay, Ruth Misener
System-Aware Neural ODE Processes for Few-Shot Bayesian Optimization. CoRR (2024)
Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk, Gunnar Rätsch, Bernhard Schölkopf
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels. ICML (2023)
Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk, Gunnar Rätsch, Bernhard Schölkopf
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels. CoRR (2023)
Harry Jake Cunningham, Daniel Augusto de Souza, So Takao, Mark van der Wilk, Marc Peter Deisenroth
Actually Sparse Variational Gaussian Processes. CoRR (2023)
Jacob Green, Cecilia Cabrera Diaz, Maximilian A. H. Jakobs, Andrea Dimitracopoulos, Mark van der Wilk, Ryan D. Greenhalgh
Current Methods for Drug Property Prediction in the Real World. CoRR (2023)
Harry Jake Cunningham, Daniel Augusto de Souza, So Takao, Mark van der Wilk, Marc Peter Deisenroth
Actually Sparse Variational Gaussian Processes. AISTATS (2023)
Tycho F. A. van der Ouderaa, Alexander Immer, Mark van der Wilk
Learning Layer-wise Equivariances Automatically using Gradients. CoRR (2023)
Jose Pablo Folch, Robert M. Lee, Behrang Shafei, David Walz, Calvin Tsay, Mark van der Wilk, Ruth Misener
Combining multi-fidelity modelling and asynchronous batch Bayesian Optimization. Comput. Chem. Eng. 172 (2023)
Anish Dhir, Mark van der Wilk
Causal Discovery using Bayesian Model Selection. CoRR (2023)
Seth Nabarro, Mark van der Wilk, Andrew J. Davison
Learning in Deep Factor Graphs with Gaussian Belief Propagation. CoRR (2023)
Tycho F. A. van der Ouderaa, Alexander Immer, Mark van der Wilk
Learning Layer-wise Equivariances Automatically using Gradients. NeurIPS (2023)
Shahin Honarvar, Mark van der Wilk, Alastair Donaldson
Turbulence: Systematically and Automatically Testing Instruction-Tuned Large Language Models for Code. CoRR (2023)
Jose Pablo Folch, James Odgers, Shiqiang Zhang, Robert M. Lee, Behrang Shafei, David Walz, Calvin Tsay, Mark van der Wilk, Ruth Misener
Practical Path-based Bayesian Optimization. CoRR (2023)
Seth Nabarro, Stoil Ganev, Adrià Garriga-Alonso, Vincent Fortuin, Mark van der Wilk, Laurence Aitchison
Data augmentation in Bayesian neural networks and the cold posterior effect. UAI (2022)
Tycho F. A. van der Ouderaa, Mark van der Wilk
Learning invariant weights in neural networks. UAI (2022)
Alexander Immer, Tycho F. A. van der Ouderaa, Vincent Fortuin, Gunnar Rätsch, Mark van der Wilk
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations. CoRR (2022)
Tycho F. A. van der Ouderaa, David W. Romero, Mark van der Wilk
Relaxing Equivariance Constraints with Non-stationary Continuous Filters. CoRR (2022)
Jose Pablo Folch, Shiqiang Zhang, Robert M. Lee, Behrang Shafei, David Walz, Calvin Tsay, Mark van der Wilk, Ruth Misener
SnAKe: Bayesian Optimization with Pathwise Exploration. CoRR (2022)
Jose Pablo Folch, Shiqiang Zhang, Robert M. Lee, Behrang Shafei, David Walz, Calvin Tsay, Mark van der Wilk, Ruth Misener
SnAKe: Bayesian Optimization with Pathwise Exploration. NeurIPS (2022)
Tycho F. A. van der Ouderaa, David W. Romero, Mark van der Wilk
Relaxing Equivariance Constraints with Non-stationary Continuous Filters. NeurIPS (2022)
Vincent Fortuin, Adrià Garriga-Alonso, Sebastian W. Ober, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison
Bayesian Neural Network Priors Revisited. ICLR (2022)
Pola Schwöbel, Martin Jørgensen, Sebastian W. Ober, Mark van der Wilk
Last Layer Marginal Likelihood for Invariance Learning. AISTATS (2022)
Jose Pablo Folch, Robert M. Lee, Behrang Shafei, David Walz, Calvin Tsay, Mark van der Wilk, Ruth Misener
Combining Multi-Fidelity Modelling and Asynchronous Batch Bayesian Optimization. CoRR (2022)
Tycho F. A. van der Ouderaa, Mark van der Wilk
Sparse Convolutions on Lie Groups. NeurReps (2022)
Alexander Immer, Tycho F. A. van der Ouderaa, Gunnar Rätsch, Vincent Fortuin, Mark van der Wilk
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations. NeurIPS (2022)
Artem Artemev, Tilman Roeder, Mark van der Wilk
Memory Safe Computations with XLA Compiler. CoRR (2022)
Alexander Terenin, David R. Burt, Artem Artemev, Seth Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees. CoRR (2022)
Artem Artemev, Yuze An, Tilman Roeder, Mark van der Wilk
Memory safe computations with XLA compiler. NeurIPS (2022)
Tycho F. A. van der Ouderaa, Mark van der Wilk
Learning Invariant Weights in Neural Networks. CoRR (2022)
Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande
Deep Neural Networks as Point Estimates for Deep Gaussian Processes. CoRR (2021)
Vincent Fortuin, Adrià Garriga-Alonso, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison
Bayesian Neural Network Priors Revisited. CoRR (2021)
Vincent Fortuin, Adrià Garriga-Alonso, Mark van der Wilk, Laurence Aitchison
BNNpriors: A library for Bayesian neural network inference with different prior distributions. CoRR (2021)
Vincent Dutordoir, Hugh Salimbeni, Eric Hambro, John McLeod, Felix Leibfried, Artem Artemev, Mark van der Wilk, James Hensman, Marc Peter Deisenroth, S. T. John
GPflux: A Library for Deep Gaussian Processes. CoRR (2021)
David R. Burt, Artem Artemev, Mark van der Wilk
Barely Biased Learning for Gaussian Process Regression. CoRR (2021)
Pola Elisabeth Schwöbel, Martin Jørgensen, Sebastian W. Ober, Mark van der Wilk
Last Layer Marginal Likelihood for Invariance Learning. CoRR (2021)
Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande
Deep Neural Networks as Point Estimates for Deep Gaussian Processes. NeurIPS (2021)
Artem Artemev, David R. Burt, Mark van der Wilk
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients. CoRR (2021)
Seth Nabarro, Stoil Ganev, Adrià Garriga-Alonso, Vincent Fortuin, Mark van der Wilk, Laurence Aitchison
Data augmentation in Bayesian neural networks and the cold posterior effect. CoRR (2021)
Sebastian W. Ober, Carl E. Rasmussen, Mark van der Wilk
The promises and pitfalls of deep kernel learning. UAI (2021)
Vincent Fortuin, Adrià Garriga-Alonso, Mark van der Wilk, Laurence Aitchison
: A library for Bayesian neural network inference with different prior distributions. Softw. Impacts 9 (2021)
Sebastian W. Ober, Carl E. Rasmussen, Mark van der Wilk
The Promises and Pitfalls of Deep Kernel Learning. CoRR (2021)
Adrià Garriga-Alonso, Mark van der Wilk
Correlated weights in infinite limits of deep convolutional neural networks. UAI (2021)
Adrià Garriga-Alonso, Mark van der Wilk
Correlated Weights in Infinite Limits of Deep Convolutional Neural Networks. CoRR (2021)
Nikolaos Mourdoukoutas, Marco Federici, Georges Pantalos, Mark van der Wilk, Vincent Fortuin
A Bayesian Approach to Invariant Deep Neural Networks. CoRR (2021)
Robin Ru, Clare Lyle, Lisa Schut, Miroslav Fil, Mark van der Wilk, Yarin Gal
Speedy Performance Estimation for Neural Architecture Search. NeurIPS (2021)
Artem Artemev, David R. Burt, Mark van der Wilk
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients. ICML (2021)
Clare Lyle, Lisa Schut, Binxin Ru, Yarin Gal, Mark van der Wilk
A Bayesian Perspective on Training Speed and Model Selection. CoRR (2020)
David R. Burt, Sebastian W. Ober, Adrià Garriga-Alonso, Mark van der Wilk
Understanding Variational Inference in Function-Space. CoRR (2020)
Clare Lyle, Mark van der Wilk, Marta Kwiatkowska, Yarin Gal, Benjamin Bloem-Reddy
On the Benefits of Invariance in Neural Networks. CoRR (2020)
Miguel Monteiro, Loïc Le Folgoc, Daniel Coelho de Castro, Nick Pawlowski, Bernardo Marques, Konstantinos Kamnitsas, Mark van der Wilk, Ben Glocker
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty. NeurIPS (2020)
Binxin Ru, Clare Lyle, Lisa Schut, Mark van der Wilk, Yarin Gal
Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search. CoRR (2020)
David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
Convergence of Sparse Variational Inference in Gaussian Processes Regression. CoRR (2020)
Clare Lyle, Lisa Schut, Robin Ru, Yarin Gal, Mark van der Wilk
A Bayesian Perspective on Training Speed and Model Selection. NeurIPS (2020)
Vincent Dutordoir, Mark van der Wilk, Artem Artemev, James Hensman
Bayesian Image Classification with Deep Convolutional Gaussian Processes. AISTATS (2020)
Miguel Monteiro, Loïc Le Folgoc, Daniel Coelho de Castro, Nick Pawlowski, Bernardo Marques, Konstantinos Kamnitsas, Mark van der Wilk, Ben Glocker
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty. CoRR (2020)
David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
Convergence of Sparse Variational Inference in Gaussian Processes Regression. J. Mach. Learn. Res. 21 (2020)
David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
Variational Orthogonal Features. CoRR (2020)
Ruby Sedgwick, John Goertz, Molly Stevens, Ruth Misener, Mark van der Wilk
Design of Experiments for Verifying Biomolecular Networks. CoRR (2020)
Mark van der Wilk, Vincent Dutordoir, S. T. John, Artem Artemev, Vincent Adam, James Hensman
A Framework for Interdomain and Multioutput Gaussian Processes. CoRR (2020)
Lewis Smith, Lisa Schut, Yarin Gal, Mark van der Wilk
Capsule Networks - A Probabilistic Perspective. CoRR (2020)
Creighton Heaukulani, Mark van der Wilk
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes. CoRR (2019)
Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models. ICML (2019)
David R. Burt, Carl E. Rasmussen, Mark van der Wilk
Rates of Convergence for Sparse Variational Gaussian Process Regression. CoRR (2019)
Dustin Tran, Mike Dusenberry, Mark van der Wilk, Danijar Hafner
Bayesian Layers: A Module for Neural Network Uncertainty. NeurIPS (2019)
Creighton Heaukulani, Mark van der Wilk
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes. NeurIPS (2019)
Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models. CoRR (2019)
David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
Rates of Convergence for Sparse Variational Gaussian Process Regression. ICML (2019)
Vincent Dutordoir, Mark van der Wilk, Artem Artemev, Marcin Tomczak, James Hensman
Translation Insensitivity for Deep Convolutional Gaussian Processes. CoRR (2019)
Mark van der Wilk, S. T. John, Artem Artemev, James Hensman
Variational Gaussian Process Models without Matrix Inverses. AABI (2019)
Alessandro Davide Ialongo, Mark van der Wilk, Carl Edward Rasmussen
Closed-form Inference and Prediction in Gaussian Process State-Space Models. CoRR (2018)
Dustin Tran, Michael W. Dusenberry, Mark van der Wilk, Danijar Hafner
Bayesian Layers: A Module for Neural Network Uncertainty. CoRR (2018)
Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen
Non-Factorised Variational Inference in Dynamical Systems. CoRR (2018)
Mark van der Wilk, Matthias Bauer, S. T. John, James Hensman
Learning Invariances using the Marginal Likelihood. NeurIPS (2018)
Mark van der Wilk, Matthias Bauer, S. T. John, James Hensman
Learning Invariances using the Marginal Likelihood. CoRR (2018)
Mark van der Wilk, Carl Edward Rasmussen, James Hensman
Convolutional Gaussian Processes. NIPS (2017)
Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo León-Villagrá, Zoubin Ghahramani, James Hensman
GPflow: A Gaussian Process Library using TensorFlow. J. Mach. Learn. Res. 18 (2017)
Rowan McAllister, Yarin Gal, Alex Kendall, Mark van der Wilk, Amar Shah, Roberto Cipolla, Adrian Weller
Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning. IJCAI (2017)
Mark van der Wilk, Carl Edward Rasmussen, James Hensman
Convolutional Gaussian Processes. CoRR (2017)
Matthias Bauer, Mark van der Wilk, Carl Edward Rasmussen
Understanding Probabilistic Sparse Gaussian Process Approximations. NIPS (2016)
Yarin Gal, Mark van der Wilk, Carl E. Rasmussen
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models. NIPS (2014)
Yarin Gal, Mark van der Wilk, Carl E. Rasmussen
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models. CoRR (2014)