Van Der Wilk, Mark

43 publications

ICLR 2025 A Meta-Learning Approach to Bayesian Causal Discovery Anish Dhir, Matthew Ashman, James Requeima, Mark van der Wilk

ICML 2025 Adjusting Model Size in Continual Gaussian Processes: How Big Is Big Enough? Guiomar Pescador-Barrios, Sarah Lucie Filippi, Mark Van Der Wilk

ICML 2025 Continuous Bayesian Model Selection for Multivariate Causal Discovery Anish Dhir, Ruby Sedgwick, Avinash Kori, Ben Glocker, Mark Van Der Wilk

NeurIPS 2025 Estimating Interventional Distributions with Uncertain Causal Graphs Through Meta-Learning Anish Dhir, Cristiana Diaconu, Valentinian Mihai Lungu, James Requeima, Richard E. Turner, Mark van der Wilk

ICML 2025 Rethinking Aleatoric and Epistemic Uncertainty Freddie Bickford Smith, Jannik Kossen, Eleanor Trollope, Mark Van Der Wilk, Adam Foster, Tom Rainforth

TMLR 2025 System-Aware Neural ODE Processes for Few-Shot Bayesian Optimization Jixiang Qing, Rebecca D. Langdon, Robert Matthew Lee, Behrang Shafei, Mark van der Wilk, Calvin Tsay, Ruth Misener

NeurIPSW 2024 Adjusting Model Size in Continual Gaussian Processes: How Big Is Big Enough? Guiomar Pescador-Barrios, Sarah Lucie Filippi, Mark van der Wilk

ICML 2024 Bivariate Causal Discovery Using Bayesian Model Selection Anish Dhir, Samuel Power, Mark Van Der Wilk

NeurIPSW 2024 Inverse-Free Sparse Variational Gaussian Processes Stefano Cortinovis, Laurence Aitchison, James Hensman, Stefanos Eleftheriadis, Mark van der Wilk

ICML 2024 Learning in Deep Factor Graphs with Gaussian Belief Propagation Seth Nabarro, Mark Van Der Wilk, Andrew Davison

NeurIPS 2024 Noether's Razor: Learning Conserved Quantities Tycho F. A. van der Ouderaa, Mark van der Wilk, Pim de Haan

JMLR 2024 Numerically Stable Sparse Gaussian Processes via Minimum Separation Using Cover Trees Alexander Terenin, David R. Burt, Artem Artemev, Seth Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge

NeurIPSW 2024 Rethinking Aleatoric and Epistemic Uncertainty Freddie Bickford Smith, Jannik Kossen, Eleanor Trollope, Mark van der Wilk, Adam Foster, Tom Rainforth

NeurIPS 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

ICMLW 2024 Variational Inference Failures Under Model Symmetries: Permutation Invariant Posteriors for Bayesian Neural Networks Yoav Gelberg, Tycho F. A. van der Ouderaa, Mark van der Wilk, Yarin Gal

NeurIPS 2023 Learning Layer-Wise Equivariances Automatically Using Gradients Tycho van der Ouderaa, Alexander Immer, Mark van der Wilk

NeurIPSW 2023 Practical Path-Based Bayesian Optimization Jose Pablo Folch, James A C Odgers, Shiqiang Zhang, Robert Matthew Lee, Behrang Shafei, David Walz, Calvin Tsay, Mark van der Wilk, Ruth Misener

ICML 2023 Stochastic Marginal Likelihood Gradients Using Neural Tangent Kernels Alexander Immer, Tycho F. A. Van Der Ouderaa, Mark Van Der Wilk, Gunnar Ratsch, Bernhard Schölkopf

AISTATS 2022 Last Layer Marginal Likelihood for Invariance Learning Pola Schwöbel, Martin Jørgensen, Sebastian W. Ober, Mark Van Der Wilk

ICLR 2022 Bayesian Neural Network Priors Revisited Vincent Fortuin, Adrià Garriga-Alonso, Sebastian W. Ober, Florian Wenzel, Gunnar Ratsch, Richard E Turner, Mark van der Wilk, Laurence Aitchison

NeurIPSW 2022 Causal Discovery Using Marginal Likelihood Anish Dhir, Mark van der Wilk

NeurIPS 2022 Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations Alexander Immer, Tycho van der Ouderaa, Gunnar Rätsch, Vincent Fortuin, Mark van der Wilk

NeurIPS 2022 Memory Safe Computations with XLA Compiler Artem Artemev, Yuze An, Tilman Roeder, Mark van der Wilk

NeurIPS 2022 Relaxing Equivariance Constraints with Non-Stationary Continuous Filters Tycho van der Ouderaa, David W. Romero, Mark van der Wilk

NeurIPS 2022 SnAKe: Bayesian Optimization with Pathwise Exploration Jose Pablo Folch, Shiqiang Zhang, Robert Lee, Behrang Shafei, David Walz, Calvin Tsay, Mark van der Wilk, Ruth Misener

NeurIPSW 2022 Sparse Convolutions on Lie Groups Tycho F.A. van der Ouderaa, Mark van der Wilk

NeurIPSW 2021 Barely Biased Learning for Gaussian Process Regression David R. Burt, Artem Artemev, Mark van der Wilk

NeurIPS 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

NeurIPS 2021 Speedy Performance Estimation for Neural Architecture Search Robin Ru, Clare Lyle, Lisa Schut, Miroslav Fil, Mark van der Wilk, Yarin Gal

NeurIPS 2020 A Bayesian Perspective on Training Speed and Model Selection Clare Lyle, Lisa Schut, Robin Ru, Yarin Gal, Mark van der Wilk

NeurIPSW 2020 Bayesian Neural Network Priors Revisited Vincent Fortuin, Adrià Garriga-Alonso, Florian Wenzel, Gunnar Ratsch, Richard E Turner, Mark van der Wilk, Laurence Aitchison

JMLR 2020 Convergence of Sparse Variational Inference in Gaussian Processes Regression David R. Burt, Carl Edward Rasmussen, Mark van der Wilk

NeurIPS 2020 Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty Miguel Monteiro, Loic Le Folgoc, Daniel Coelho de Castro, Nick Pawlowski, Bernardo Marques, Konstantinos Kamnitsas, Mark van der Wilk, Ben Glocker

NeurIPS 2019 Bayesian Layers: A Module for Neural Network Uncertainty Dustin Tran, Mike Dusenberry, Mark van der Wilk, Danijar Hafner

ICML 2019 Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models Alessandro Davide Ialongo, Mark Van Der Wilk, James Hensman, Carl Edward Rasmussen

ICML 2019 Rates of Convergence for Sparse Variational Gaussian Process Regression David Burt, Carl Edward Rasmussen, Mark Van Der Wilk

NeurIPS 2019 Scalable Bayesian Dynamic Covariance Modeling with Variational Wishart and Inverse Wishart Processes Creighton Heaukulani, Mark van der Wilk

NeurIPS 2018 Learning Invariances Using the Marginal Likelihood Mark van der Wilk, Matthias Bauer, St John, James Hensman

IJCAI 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

NeurIPS 2017 Convolutional Gaussian Processes Mark van der Wilk, Carl Edward Rasmussen, James Hensman

MLOSS 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

NeurIPS 2016 Understanding Probabilistic Sparse Gaussian Process Approximations Matthias Bauer, Mark van der Wilk, Carl Edward Rasmussen

NeurIPS 2014 Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models Yarin Gal, Mark van der Wilk, Carl Edward Rasmussen