Mark van der Wilk

109 papers A* 26A 8Journal 67Unranked 7

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Distributed Gaussian Process Model for Multi-Robot Mapping. CoRR Seth Nabarro, Mark van der Wilk, Andrew J. Davison
2026	J	`jnl`	Use What You Know: Causal Foundation Models with Partial Graphs. CoRR Arik Reuter, Anish Dhir, Cristiana Diaconu, Jake Robertson, Ole Ossen, Frank Hutter, Adrian Weller, Mark van der Wilk, Bernhard Schölkopf
2025	A*	`conf`	A Meta-Learning Approach to Bayesian Causal Discovery. ICLR Anish Dhir, Matthew Ashman, James Requeima, Mark van der Wilk
2025	A*	`conf`	Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough? ICML Guiomar Pescador-Barrios, Sarah Filippi, Mark van der Wilk
2025	A*	`conf`	Continuous Bayesian Model Selection for Multivariate Causal Discovery. ICML Anish Dhir, Ruby Sedgwick, Avinash Kori, Ben Glocker, Mark van der Wilk
2025	J	`jnl`	Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning. CoRR Anish Dhir, Cristiana Diaconu, Valentinian Mihai Lungu, James Requeima, Richard E. Turner, Mark van der Wilk
2025	—	`conf`	PSyDUCK: Hiding Information in the Denoising Process of Latent Diffusion Models. WIFS Aqib Mahfuz, Georgia Channing, Mark van der Wilk, Philip H. S. Torr, Fabio Pizzati, Christian Schroeder de Witt
2025	J	`jnl`	PSyDUCK: Training-Free Steganography for Latent Diffusion. CoRR Georgia Channing, Aqib Mahfuz, Mark van der Wilk, Philip Torr, Fabio Pizzati, Christian Schröder de Witt
2025	A*	`conf`	Rethinking Aleatoric and Epistemic Uncertainty. ICML Freddie Bickford Smith, Jannik Kossen, Eleanor Trollope, Mark van der Wilk, Adam Foster, Tom Rainforth
2025	J	`jnl`	SynDaCaTE: A Synthetic Dataset For Evaluating Part-Whole Hierarchical Inference. CoRR Jake Levi, Mark van der Wilk
2025	J	`jnl`	System-Aware Neural ODE Processes for Few-Shot Bayesian Optimization. Trans. Mach. Learn. Res. Jixiang Qing, Rebecca D. Langdon, Robert M. Lee, Behrang Shafei, Mark van der Wilk, Calvin Tsay, Ruth Misener
2025	A	`conf`	Turbulence: Systematically and Automatically Testing Instruction-Tuned Large Language Models for Code. ICST Shahin Honarvar, Mark van der Wilk, Alastair F. Donaldson
2024	J	`jnl`	"How Big is Big Enough?" Adjusting Model Size in Continual Gaussian Processes. CoRR Guiomar Pescador-Barrios, Sarah Filippi, Mark van der Wilk
2024	J	`jnl`	A Meta-Learning Approach to Bayesian Causal Discovery. CoRR Anish Dhir, Matthew Ashman, James Requeima, Mark van der Wilk
2024	A*	`conf`	Bivariate Causal Discovery using Bayesian Model Selection. ICML Anish Dhir, Samuel Power, Mark van der Wilk
2024	J	`jnl`	Continuous Bayesian Model Selection for Multivariate Causal Discovery. CoRR Anish Dhir, Ruby Sedgwick, Avinash Kori, Ben Glocker, Mark van der Wilk
2024	A*	`conf`	Learning in Deep Factor Graphs with Gaussian Belief Propagation. ICML Seth Nabarro, Mark van der Wilk, Andrew J. Davison
2024	A*	`conf`	Noether's Razor: Learning Conserved Quantities. NeurIPS Tycho F. A. van der Ouderaa, Mark van der Wilk, Pim de Haan
2024	J	`jnl`	Noether's razor: Learning Conserved Quantities. CoRR Tycho F. A. van der Ouderaa, Mark van der Wilk, Pim de Haan
2024	J	`jnl`	Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees. J. Mach. Learn. Res. Alexander Terenin, David R. Burt, Artem Artemev, Seth R. Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge
2024	J	`jnl`	Recommendations for Baselines and Benchmarking Approximate Gaussian Processes. CoRR Sebastian W. Ober, Artem Artemev, Marcel Wagenländer, Rudolfs Grobins, Mark van der Wilk
2024	J	`jnl`	Rethinking Aleatoric and Epistemic Uncertainty. CoRR Freddie Bickford Smith, Jannik Kossen, Eleanor Trollope, Mark van der Wilk, Adam Foster, Tom Rainforth
2024	J	`jnl`	Rethinking the Role of Bayesianism in the Age of Modern AI (Dagstuhl Seminar 24461). Dagstuhl Reports Vincent Fortuin, Mohammad Emtiyaz Khan, Mark van der Wilk, Zoubin Ghahramani, Katharine Fisher
2024	J	`jnl`	System-Aware Neural ODE Processes for Few-Shot Bayesian Optimization. CoRR Jixiang Qing, Becky D. Langdon, Robert M. Lee, Behrang Shafei, Mark van der Wilk, Calvin Tsay, Ruth Misener
2024	J	`jnl`	Transfer Learning Bayesian Optimization to Design Competitor DNA Molecules for Use in Diagnostic Assays. CoRR Ruby Sedgwick, John P. Goertz, Molly M. Stevens, Ruth Misener, Mark van der Wilk
2024	A*	`conf`	Transition Constrained Bayesian Optimization via Markov Decision Processes. NeurIPS Jose Pablo Folch, Calvin Tsay, Robert M. Lee, Behrang Shafei, Weronika Ormaniec, Andreas Krause, Mark van der Wilk, Ruth Misener, Mojmir Mutny
2024	J	`jnl`	Transition Constrained Bayesian Optimization via Markov Decision Processes. CoRR Jose Pablo Folch, Calvin Tsay, Robert M. Lee, Behrang Shafei, Weronika Ormaniec, Andreas Krause, Mark van der Wilk, Ruth Misener, Mojmír Mutný
2024	—	`conf`	Variational Inference Failures Under Model Symmetries: Permutation Invariant Posteriors for Bayesian Neural Networks. GRaM Yoav Gelberg, Tycho F. A. van der Ouderaa, Mark van der Wilk, Yarin Gal
2024	J	`jnl`	Variational Inference Failures Under Model Symmetries: Permutation Invariant Posteriors for Bayesian Neural Networks. CoRR Yoav Gelberg, Tycho F. A. van der Ouderaa, Mark van der Wilk, Yarin Gal
2023	A	`conf`	Actually Sparse Variational Gaussian Processes. AISTATS Harry Jake Cunningham, Daniel Augusto de Souza, So Takao, Mark van der Wilk, Marc Peter Deisenroth
2023	J	`jnl`	Actually Sparse Variational Gaussian Processes. CoRR Harry Jake Cunningham, Daniel Augusto de Souza, So Takao, Mark van der Wilk, Marc Peter Deisenroth
2023	J	`jnl`	Causal Discovery using Bayesian Model Selection. CoRR Anish Dhir, Mark van der Wilk
2023	J	`jnl`	Combining multi-fidelity modelling and asynchronous batch Bayesian Optimization. Comput. Chem. Eng. Jose Pablo Folch, Robert M. Lee, Behrang Shafei, David Walz, Calvin Tsay, Mark van der Wilk, Ruth Misener
2023	J	`jnl`	Current Methods for Drug Property Prediction in the Real World. CoRR Jacob Green, Cecilia Cabrera Diaz, Maximilian A. H. Jakobs, Andrea Dimitracopoulos, Mark van der Wilk, Ryan D. Greenhalgh
2023	A*	`conf`	Learning Layer-wise Equivariances Automatically using Gradients. NeurIPS Tycho F. A. van der Ouderaa, Alexander Immer, Mark van der Wilk
2023	J	`jnl`	Learning Layer-wise Equivariances Automatically using Gradients. CoRR Tycho F. A. van der Ouderaa, Alexander Immer, Mark van der Wilk
2023	J	`jnl`	Learning in Deep Factor Graphs with Gaussian Belief Propagation. CoRR Seth Nabarro, Mark van der Wilk, Andrew J. Davison
2023	J	`jnl`	Practical Path-based Bayesian Optimization. CoRR Jose Pablo Folch, James Odgers, Shiqiang Zhang, Robert M. Lee, Behrang Shafei, David Walz, Calvin Tsay, Mark van der Wilk, Ruth Misener
2023	A*	`conf`	Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels. ICML Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk, Gunnar Rätsch, Bernhard Schölkopf
2023	J	`jnl`	Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels. CoRR Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk, Gunnar Rätsch, Bernhard Schölkopf
2023	J	`jnl`	Turbulence: Systematically and Automatically Testing Instruction-Tuned Large Language Models for Code. CoRR Shahin Honarvar, Mark van der Wilk, Alastair F. Donaldson
2022	A*	`conf`	Bayesian Neural Network Priors Revisited. ICLR Vincent Fortuin, Adrià Garriga-Alonso, Sebastian W. Ober, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison
2022	J	`jnl`	Combining Multi-Fidelity Modelling and Asynchronous Batch Bayesian Optimization. CoRR Jose Pablo Folch, Robert M. Lee, Behrang Shafei, David Walz, Calvin Tsay, Mark van der Wilk, Ruth Misener
2022	A	`conf`	Data augmentation in Bayesian neural networks and the cold posterior effect. UAI Seth Nabarro, Stoil Ganev, Adrià Garriga-Alonso, Vincent Fortuin, Mark van der Wilk, Laurence Aitchison
2022	A*	`conf`	Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations. NeurIPS Alexander Immer, Tycho F. A. van der Ouderaa, Gunnar Rätsch, Vincent Fortuin, Mark van der Wilk
2022	J	`jnl`	Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations. CoRR Alexander Immer, Tycho F. A. van der Ouderaa, Vincent Fortuin, Gunnar Rätsch, Mark van der Wilk
2022	A	`conf`	Last Layer Marginal Likelihood for Invariance Learning. AISTATS Pola Schwöbel, Martin Jørgensen, Sebastian W. Ober, Mark van der Wilk
2022	J	`jnl`	Learning Invariant Weights in Neural Networks. CoRR Tycho F. A. van der Ouderaa, Mark van der Wilk
2022	A	`conf`	Learning invariant weights in neural networks. UAI Tycho F. A. van der Ouderaa, Mark van der Wilk
2022	J	`jnl`	Memory Safe Computations with XLA Compiler. CoRR Artem Artemev, Tilman Roeder, Mark van der Wilk
2022	A*	`conf`	Memory safe computations with XLA compiler. NeurIPS Artem Artemev, Yuze An, Tilman Roeder, Mark van der Wilk
2022	J	`jnl`	Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees. CoRR Alexander Terenin, David R. Burt, Artem Artemev, Seth R. Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge
2022	A*	`conf`	Relaxing Equivariance Constraints with Non-stationary Continuous Filters. NeurIPS Tycho F. A. van der Ouderaa, David W. Romero, Mark van der Wilk
2022	J	`jnl`	Relaxing Equivariance Constraints with Non-stationary Continuous Filters. CoRR Tycho F. A. van der Ouderaa, David W. Romero, Mark van der Wilk
2022	A*	`conf`	SnAKe: Bayesian Optimization with Pathwise Exploration. NeurIPS Jose Pablo Folch, Shiqiang Zhang, Robert M. Lee, Behrang Shafei, David Walz, Calvin Tsay, Mark van der Wilk, Ruth Misener
2022	J	`jnl`	SnAKe: Bayesian Optimization with Pathwise Exploration. CoRR Jose Pablo Folch, Shiqiang Zhang, Robert M. Lee, Behrang Shafei, David Walz, Calvin Tsay, Mark van der Wilk, Ruth Misener
2022	—	`conf`	Sparse Convolutions on Lie Groups. NeurReps Tycho F. A. van der Ouderaa, Mark van der Wilk
2021	J	`jnl`	A Bayesian Approach to Invariant Deep Neural Networks. CoRR Nikolaos Mourdoukoutas, Marco Federici, Georges Pantalos, Mark van der Wilk, Vincent Fortuin
2021	J	`jnl`	BNNpriors: A library for Bayesian neural network inference with different prior distributions. CoRR Vincent Fortuin, Adrià Garriga-Alonso, Mark van der Wilk, Laurence Aitchison
2021	J	`jnl`	Barely Biased Learning for Gaussian Process Regression. CoRR David R. Burt, Artem Artemev, Mark van der Wilk
2021	J	`jnl`	Bayesian Neural Network Priors Revisited. CoRR Vincent Fortuin, Adrià Garriga-Alonso, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison
2021	J	`jnl`	Correlated Weights in Infinite Limits of Deep Convolutional Neural Networks. CoRR Adrià Garriga-Alonso, Mark van der Wilk
2021	A	`conf`	Correlated weights in infinite limits of deep convolutional neural networks. UAI Adrià Garriga-Alonso, Mark van der Wilk
2021	J	`jnl`	Data augmentation in Bayesian neural networks and the cold posterior effect. CoRR Seth Nabarro, Stoil Ganev, Adrià Garriga-Alonso, Vincent Fortuin, Mark van der Wilk, Laurence Aitchison
2021	A*	`conf`	Deep Neural Networks as Point Estimates for Deep Gaussian Processes. NeurIPS Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande
2021	J	`jnl`	Deep Neural Networks as Point Estimates for Deep Gaussian Processes. CoRR Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande
2021	J	`jnl`	GPflux: A Library for Deep Gaussian Processes. CoRR Vincent Dutordoir, Hugh Salimbeni, Eric Hambro, John McLeod, Felix Leibfried, Artem Artemev, Mark van der Wilk, James Hensman, Marc Peter Deisenroth, S. T. John
2021	J	`jnl`	Last Layer Marginal Likelihood for Invariance Learning. CoRR Pola Elisabeth Schwöbel, Martin Jørgensen, Sebastian W. Ober, Mark van der Wilk
2021	A*	`conf`	Speedy Performance Estimation for Neural Architecture Search. NeurIPS Robin Ru, Clare Lyle, Lisa Schut, Miroslav Fil, Mark van der Wilk, Yarin Gal
2021	J	`jnl`	The Promises and Pitfalls of Deep Kernel Learning. CoRR Sebastian W. Ober, Carl E. Rasmussen, Mark van der Wilk
2021	A	`conf`	The promises and pitfalls of deep kernel learning. UAI Sebastian W. Ober, Carl E. Rasmussen, Mark van der Wilk
2021	A*	`conf`	Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients. ICML Artem Artemev, David R. Burt, Mark van der Wilk
2021	J	`jnl`	Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients. CoRR Artem Artemev, David R. Burt, Mark van der Wilk
2020	A*	`conf`	A Bayesian Perspective on Training Speed and Model Selection. NeurIPS Clare Lyle, Lisa Schut, Binxin Ru, Yarin Gal, Mark van der Wilk
2020	J	`jnl`	A Bayesian Perspective on Training Speed and Model Selection. CoRR Clare Lyle, Lisa Schut, Binxin Ru, Yarin Gal, Mark van der Wilk
2020	J	`jnl`	A Framework for Interdomain and Multioutput Gaussian Processes. CoRR Mark van der Wilk, Vincent Dutordoir, S. T. John, Artem Artemev, Vincent Adam, James Hensman
2020	A	`conf`	Bayesian Image Classification with Deep Convolutional Gaussian Processes. AISTATS Vincent Dutordoir, Mark van der Wilk, Artem Artemev, James Hensman
2020	J	`jnl`	Capsule Networks - A Probabilistic Perspective. CoRR Lewis Smith, Lisa Schut, Yarin Gal, Mark van der Wilk
2020	J	`jnl`	Convergence of Sparse Variational Inference in Gaussian Processes Regression. CoRR David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
2020	J	`jnl`	Convergence of Sparse Variational Inference in Gaussian Processes Regression. J. Mach. Learn. Res. David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
2020	J	`jnl`	Design of Experiments for Verifying Biomolecular Networks. CoRR Ruby Sedgwick, John P. Goertz, Molly M. Stevens, Ruth Misener, Mark van der Wilk
2020	J	`jnl`	On the Benefits of Invariance in Neural Networks. CoRR Clare Lyle, Mark van der Wilk, Marta Kwiatkowska, Yarin Gal, Benjamin Bloem-Reddy
2020	J	`jnl`	Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search. CoRR Binxin Ru, Clare Lyle, Lisa Schut, Mark van der Wilk, Yarin Gal
2020	A*	`conf`	Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty. NeurIPS Miguel Monteiro, Loïc Le Folgoc, Daniel Coelho de Castro, Nick Pawlowski, Bernardo Marques, Konstantinos Kamnitsas, Mark van der Wilk, Ben Glocker
2020	J	`jnl`	Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty. CoRR Miguel Monteiro, Loïc Le Folgoc, Daniel Coelho de Castro, Nick Pawlowski, Bernardo Marques, Konstantinos Kamnitsas, Mark van der Wilk, Ben Glocker
2020	J	`jnl`	Understanding Variational Inference in Function-Space. CoRR David R. Burt, Sebastian W. Ober, Adrià Garriga-Alonso, Mark van der Wilk
2020	J	`jnl`	Variational Orthogonal Features. CoRR David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
2019	A*	`conf`	Bayesian Layers: A Module for Neural Network Uncertainty. NeurIPS Dustin Tran, Mike Dusenberry, Mark van der Wilk, Danijar Hafner
2019	A*	`conf`	Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models. ICML Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen
2019	J	`jnl`	Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models. CoRR Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen
2019	A*	`conf`	Rates of Convergence for Sparse Variational Gaussian Process Regression. ICML David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
2019	J	`jnl`	Rates of Convergence for Sparse Variational Gaussian Process Regression. CoRR David R. Burt, Carl E. Rasmussen, Mark van der Wilk
2019	A*	`conf`	Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes. NeurIPS Creighton Heaukulani, Mark van der Wilk
2019	J	`jnl`	Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes. CoRR Creighton Heaukulani, Mark van der Wilk
2019	—	`—`	Sparse Gaussian process approximations and applications. Mark van der Wilk
2019	J	`jnl`	Translation Insensitivity for Deep Convolutional Gaussian Processes. CoRR Vincent Dutordoir, Mark van der Wilk, Artem Artemev, Marcin Tomczak, James Hensman
2019	—	`conf`	Variational Gaussian Process Models without Matrix Inverses. AABI Mark van der Wilk, S. T. John, Artem Artemev, James Hensman
2018	J	`jnl`	Bayesian Layers: A Module for Neural Network Uncertainty. CoRR Dustin Tran, Michael W. Dusenberry, Mark van der Wilk, Danijar Hafner
2018	J	`jnl`	Closed-form Inference and Prediction in Gaussian Process State-Space Models. CoRR Alessandro Davide Ialongo, Mark van der Wilk, Carl Edward Rasmussen
2018	A*	`conf`	Learning Invariances using the Marginal Likelihood. NeurIPS Mark van der Wilk, Matthias Bauer, S. T. John, James Hensman
2018	J	`jnl`	Learning Invariances using the Marginal Likelihood. CoRR Mark van der Wilk, Matthias Bauer, S. T. John, James Hensman
2018	J	`jnl`	Non-Factorised Variational Inference in Dynamical Systems. CoRR Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen
2017	A*	`conf`	Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning. IJCAI Rowan McAllister, Yarin Gal, Alex Kendall, Mark van der Wilk, Amar Shah, Roberto Cipolla, Adrian Weller
2017	—	`conf`	Convolutional Gaussian Processes. NIPS Mark van der Wilk, Carl Edward Rasmussen, James Hensman
2017	J	`jnl`	Convolutional Gaussian Processes. CoRR Mark van der Wilk, Carl Edward Rasmussen, James Hensman
2017	J	`jnl`	GPflow: A Gaussian Process Library using TensorFlow. J. Mach. Learn. Res. Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo León-Villagrá, Zoubin Ghahramani, James Hensman
2016	—	`conf`	Understanding Probabilistic Sparse Gaussian Process Approximations. NIPS Matthias Bauer, Mark van der Wilk, Carl Edward Rasmussen
2014	—	`conf`	Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models. NIPS Yarin Gal, Mark van der Wilk, Carl E. Rasmussen
2014	J	`jnl`	Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models. CoRR Yarin Gal, Mark van der Wilk, Carl E. Rasmussen