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ORCIDMark van der Wilk
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- affiliation: Imperial College London, UK
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2020 – today
- 2025
[i58]Georgia Channing, Aqib Mahfuz, Mark van der Wilk, Philip Torr, Fabio Pizzati, Christian Schröder de Witt:
PSyDUCK: Training-Free Steganography for Latent Diffusion. CoRR abs/2501.19172 (2025)- 2024
- [j5]Alexander Terenin, David R. Burt, Artem Artemev, Seth R. Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge:
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees. J. Mach. Learn. Res. 25: 26:1-26:36 (2024) - [c34]Anish Dhir, Samuel Power, Mark van der Wilk:
Bivariate Causal Discovery using Bayesian Model Selection. ICML 2024 - [c33]Seth Nabarro, Mark van der Wilk, Andrew J. Davison:
Learning in Deep Factor Graphs with Gaussian Belief Propagation. ICML 2024 - [c32]Jose Pablo Folch, Calvin Tsay, Robert M. Lee, Behrang Shafei, Weronika Ormaniec, Andreas Krause, Mark van der Wilk, Ruth Misener, Mojmir Mutny:
Transition Constrained Bayesian Optimization via Markov Decision Processes. NeurIPS 2024 - [c31]Tycho F. A. van der Ouderaa, Mark van der Wilk, Pim de Haan:
Noether's Razor: Learning Conserved Quantities. NeurIPS 2024 - [i57]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 abs/2402.08406 (2024) - [i56]Sebastian W. Ober, Artem Artemev, Marcel Wagenländer, Rudolfs Grobins, Mark van der Wilk:
Recommendations for Baselines and Benchmarking Approximate Gaussian Processes. CoRR abs/2402.09849 (2024) - [i55]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 abs/2402.17704 (2024) - [i54]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 abs/2406.02352 (2024) - [i53]Yoav Gelberg, Tycho F. A. van der Ouderaa, Mark van der Wilk, Yarin Gal:
Variational Inference Failures Under Model Symmetries: Permutation Invariant Posteriors for Bayesian Neural Networks. CoRR abs/2408.05496 (2024) - [i52]Guiomar Pescador-Barrios, Sarah Filippi, Mark van der Wilk:
"How Big is Big Enough?" Adjusting Model Size in Continual Gaussian Processes. CoRR abs/2408.07588 (2024) - [i51]Tycho F. A. van der Ouderaa, Mark van der Wilk, Pim de Haan:
Noether's razor: Learning Conserved Quantities. CoRR abs/2410.08087 (2024) - [i50]Anish Dhir, Ruby Sedgwick, Avinash Kori, Ben Glocker, Mark van der Wilk:
Continuous Bayesian Model Selection for Multivariate Causal Discovery. CoRR abs/2411.10154 (2024) - [i49]Anish Dhir, Matthew Ashman, James Requeima, Mark van der Wilk:
A Meta-Learning Approach to Bayesian Causal Discovery. CoRR abs/2412.16577 (2024) - [i48]Freddie Bickford Smith, Jannik Kossen, Eleanor Trollope, Mark van der Wilk, Adam Foster, Tom Rainforth:
Rethinking Aleatoric and Epistemic Uncertainty. CoRR abs/2412.20892 (2024) - [i47]Vincent Fortuin, Mohammad Emtiyaz Khan, Mark van der Wilk, Zoubin Ghahramani, Katharine Fisher:
Rethinking the Role of Bayesianism in the Age of Modern AI (Dagstuhl Seminar 24461). Dagstuhl Reports 14(11): 40-59 (2024) - 2023
- [j4]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: 108194 (2023) - [c30]Harry Jake Cunningham, Daniel Augusto de Souza, So Takao, Mark van der Wilk, Marc Peter Deisenroth:
Actually Sparse Variational Gaussian Processes. AISTATS 2023: 10395-10408 - [c29]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: 14333-14352 - [c28]Tycho F. A. van der Ouderaa, Alexander Immer, Mark van der Wilk:
Learning Layer-wise Equivariances Automatically using Gradients. NeurIPS 2023 - [i46]Harry Jake Cunningham, Daniel Augusto de Souza, So Takao, Mark van der Wilk, Marc Peter Deisenroth:
Actually Sparse Variational Gaussian Processes. CoRR abs/2304.05091 (2023) - [i45]Anish Dhir, Mark van der Wilk:
Causal Discovery using Bayesian Model Selection. CoRR abs/2306.02931 (2023) - [i44]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 abs/2306.03968 (2023) - [i43]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 abs/2309.17161 (2023) - [i42]Tycho F. A. van der Ouderaa, Alexander Immer, Mark van der Wilk:
Learning Layer-wise Equivariances Automatically using Gradients. CoRR abs/2310.06131 (2023) - [i41]Seth Nabarro, Mark van der Wilk, Andrew J. Davison:
Learning in Deep Factor Graphs with Gaussian Belief Propagation. CoRR abs/2311.14649 (2023) - [i40]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 abs/2312.00622 (2023) - [i39]Shahin Honarvar, Mark van der Wilk, Alastair F. Donaldson:
Turbulence: Systematically and Automatically Testing Instruction-Tuned Large Language Models for Code. CoRR abs/2312.14856 (2023) - 2022
- [c27]Pola Schwöbel, Martin Jørgensen, Sebastian W. Ober, Mark van der Wilk:
Last Layer Marginal Likelihood for Invariance Learning. AISTATS 2022: 3542-3555 - [c26]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 - [c25]Tycho F. A. van der Ouderaa, Mark van der Wilk:
Sparse Convolutions on Lie Groups. NeurReps 2022: 48-62 - [c24]Artem Artemev, Yuze An, Tilman Roeder, Mark van der Wilk:
Memory safe computations with XLA compiler. NeurIPS 2022 - [c23]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 - [c22]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 - [c21]Tycho F. A. van der Ouderaa, David W. Romero, Mark van der Wilk:
Relaxing Equivariance Constraints with Non-stationary Continuous Filters. NeurIPS 2022 - [c20]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: 1434-1444 - [c19]Tycho F. A. van der Ouderaa, Mark van der Wilk:
Learning invariant weights in neural networks. UAI 2022: 1992-2001 - [i38]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 abs/2202.00060 (2022) - [i37]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 abs/2202.10638 (2022) - [i36]Tycho F. A. van der Ouderaa, Mark van der Wilk:
Learning Invariant Weights in Neural Networks. CoRR abs/2202.12439 (2022) - [i35]Tycho F. A. van der Ouderaa, David W. Romero, Mark van der Wilk:
Relaxing Equivariance Constraints with Non-stationary Continuous Filters. CoRR abs/2204.07178 (2022) - [i34]Artem Artemev, Tilman Roeder, Mark van der Wilk:
Memory Safe Computations with XLA Compiler. CoRR abs/2206.14148 (2022) - [i33]Alexander Terenin, David R. Burt, Artem Artemev, Seth R. Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge:
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees. CoRR abs/2210.07893 (2022) - [i32]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 abs/2211.06149 (2022) - 2021
- [j3]Vincent Fortuin, Adrià Garriga-Alonso, Mark van der Wilk, Laurence Aitchison:
BNNpriors: A library for Bayesian neural network inference with different prior distributions. Softw. Impacts 9: 100079 (2021) - [c18]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: 362-372 - [c17]Robin Ru, Clare Lyle, Lisa Schut, Miroslav Fil, Mark van der Wilk, Yarin Gal:
Speedy Performance Estimation for Neural Architecture Search. NeurIPS 2021: 4079-4092 - [c16]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: 9443-9455 - [c15]Sebastian W. Ober, Carl E. Rasmussen, Mark van der Wilk:
The promises and pitfalls of deep kernel learning. UAI 2021: 1206-1216 - [c14]Adrià Garriga-Alonso, Mark van der Wilk:
Correlated weights in infinite limits of deep convolutional neural networks. UAI 2021: 1998-2007 - [i31]Adrià Garriga-Alonso, Mark van der Wilk:
Correlated Weights in Infinite Limits of Deep Convolutional Neural Networks. CoRR abs/2101.04097 (2021) - [i30]Vincent Fortuin, Adrià Garriga-Alonso, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison:
Bayesian Neural Network Priors Revisited. CoRR abs/2102.06571 (2021) - [i29]Artem Artemev, David R. Burt, Mark van der Wilk:
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients. CoRR abs/2102.08314 (2021) - [i28]Sebastian W. Ober, Carl E. Rasmussen, Mark van der Wilk:
The Promises and Pitfalls of Deep Kernel Learning. CoRR abs/2102.12108 (2021) - [i27]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 abs/2104.05674 (2021) - [i26]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 abs/2105.04504 (2021) - [i25]Vincent Fortuin, Adrià Garriga-Alonso, Mark van der Wilk, Laurence Aitchison:
BNNpriors: A library for Bayesian neural network inference with different prior distributions. CoRR abs/2105.06964 (2021) - [i24]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 abs/2106.05586 (2021) - [i23]Pola Elisabeth Schwöbel, Martin Jørgensen, Sebastian W. Ober, Mark van der Wilk:
Last Layer Marginal Likelihood for Invariance Learning. CoRR abs/2106.07512 (2021) - [i22]Nikolaos Mourdoukoutas, Marco Federici, Georges Pantalos, Mark van der Wilk, Vincent Fortuin:
A Bayesian Approach to Invariant Deep Neural Networks. CoRR abs/2107.09301 (2021) - [i21]David R. Burt, Artem Artemev, Mark van der Wilk:
Barely Biased Learning for Gaussian Process Regression. CoRR abs/2109.09417 (2021) - 2020
- [j2]David R. Burt, Carl Edward Rasmussen, Mark van der Wilk:
Convergence of Sparse Variational Inference in Gaussian Processes Regression. J. Mach. Learn. Res. 21: 131:1-131:63 (2020) - [c13]Vincent Dutordoir, Mark van der Wilk, Artem Artemev, James Hensman:
Bayesian Image Classification with Deep Convolutional Gaussian Processes. AISTATS 2020: 1529-1539 - [c12]Clare Lyle, Lisa Schut, Robin Ru, Yarin Gal, Mark van der Wilk:
A Bayesian Perspective on Training Speed and Model Selection. NeurIPS 2020 - [c11]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 - [i20]Mark van der Wilk, Vincent Dutordoir, S. T. John, Artem Artemev, Vincent Adam, James Hensman:
A Framework for Interdomain and Multioutput Gaussian Processes. CoRR abs/2003.01115 (2020) - [i19]Lewis Smith, Lisa Schut, Yarin Gal, Mark van der Wilk:
Capsule Networks - A Probabilistic Perspective. CoRR abs/2004.03553 (2020) - [i18]Clare Lyle, Mark van der Wilk, Marta Kwiatkowska, Yarin Gal, Benjamin Bloem-Reddy:
On the Benefits of Invariance in Neural Networks. CoRR abs/2005.00178 (2020) - [i17]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 abs/2006.04492 (2020) - [i16]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 abs/2006.06015 (2020) - [i15]David R. Burt, Carl Edward Rasmussen, Mark van der Wilk:
Variational Orthogonal Features. CoRR abs/2006.13170 (2020) - [i14]David R. Burt, Carl Edward Rasmussen, Mark van der Wilk:
Convergence of Sparse Variational Inference in Gaussian Processes Regression. CoRR abs/2008.00323 (2020) - [i13]Clare Lyle, Lisa Schut, Binxin Ru, Yarin Gal, Mark van der Wilk:
A Bayesian Perspective on Training Speed and Model Selection. CoRR abs/2010.14499 (2020) - [i12]David R. Burt, Sebastian W. Ober, Adrià Garriga-Alonso, Mark van der Wilk:
Understanding Variational Inference in Function-Space. CoRR abs/2011.09421 (2020) - [i11]Ruby Sedgwick, John P. Goertz, Molly M. Stevens, Ruth Misener, Mark van der Wilk:
Design of Experiments for Verifying Biomolecular Networks. CoRR abs/2011.10575 (2020)
2010 – 2019
- 2019
- [b1]Mark van der Wilk:
Sparse Gaussian process approximations and applications. University of Cambridge, UK, 2019 - [c10]Mark van der Wilk, S. T. John, Artem Artemev, James Hensman:
Variational Gaussian Process Models without Matrix Inverses. AABI 2019: 1-9 - [c9]David R. Burt, Carl Edward Rasmussen, Mark van der Wilk:
Rates of Convergence for Sparse Variational Gaussian Process Regression. ICML 2019: 862-871 - [c8]Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen:
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models. ICML 2019: 2931-2940 - [c7]Creighton Heaukulani, Mark van der Wilk:
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes. NeurIPS 2019: 4584-4594 - [c6]Dustin Tran, Mike Dusenberry, Mark van der Wilk, Danijar Hafner:
Bayesian Layers: A Module for Neural Network Uncertainty. NeurIPS 2019: 14633-14645 - [i10]Vincent Dutordoir, Mark van der Wilk, Artem Artemev, Marcin Tomczak, James Hensman:
Translation Insensitivity for Deep Convolutional Gaussian Processes. CoRR abs/1902.05888 (2019) - [i9]David R. Burt, Carl E. Rasmussen, Mark van der Wilk:
Rates of Convergence for Sparse Variational Gaussian Process Regression. CoRR abs/1903.03571 (2019) - [i8]Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen:
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models. CoRR abs/1906.05828 (2019) - [i7]Creighton Heaukulani, Mark van der Wilk:
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes. CoRR abs/1906.09360 (2019) - 2018
- [c5]Mark van der Wilk, Matthias Bauer, S. T. John, James Hensman:
Learning Invariances using the Marginal Likelihood. NeurIPS 2018: 9960-9970 - [i6]Mark van der Wilk, Matthias Bauer, S. T. John, James Hensman:
Learning Invariances using the Marginal Likelihood. CoRR abs/1808.05563 (2018) - [i5]Alessandro Davide Ialongo, Mark van der Wilk, Carl Edward Rasmussen:
Closed-form Inference and Prediction in Gaussian Process State-Space Models. CoRR abs/1812.03580 (2018) - [i4]Dustin Tran, Michael W. Dusenberry, Mark van der Wilk, Danijar Hafner:
Bayesian Layers: A Module for Neural Network Uncertainty. CoRR abs/1812.03973 (2018) - [i3]Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen:
Non-Factorised Variational Inference in Dynamical Systems. CoRR abs/1812.06067 (2018) - 2017
- [j1]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: 40:1-40:6 (2017) - [c4]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: 4745-4753 - [c3]Mark van der Wilk, Carl Edward Rasmussen, James Hensman:
Convolutional Gaussian Processes. NIPS 2017: 2849-2858 - [i2]Mark van der Wilk, Carl Edward Rasmussen, James Hensman:
Convolutional Gaussian Processes. CoRR abs/1709.01894 (2017) - 2016
- [c2]Matthias Bauer, Mark van der Wilk, Carl Edward Rasmussen:
Understanding Probabilistic Sparse Gaussian Process Approximations. NIPS 2016: 1525-1533 - 2014
- [c1]Yarin Gal, Mark van der Wilk, Carl E. Rasmussen:
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models. NIPS 2014: 3257-3265 - [i1]Yarin Gal, Mark van der Wilk, Carl E. Rasmussen:
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models. CoRR abs/1402.1389 (2014)
Coauthor Index
Carl E. Rasmussen
aka: Carl Edward Rasmussen
aka: Carl Edward Rasmussen
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