Mark van der Wilk
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
20
Venues
6
Active years
2017–2025
Best venue rank
A*
Where they publish
Papers
20 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | ICLR | A Meta-Learning Approach to Bayesian Causal Discovery. | Anish Dhir, Matthew Ashman, James Requeima, Mark van der Wilk |
| 2025 | ICML | Continuous Bayesian Model Selection for Multivariate Causal Discovery. | Anish Dhir, Ruby Sedgwick, Avinash Kori, Ben Glocker, Mark van der Wilk |
| 2025 | ICML | Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough? | Guiomar Pescador-Barrios, Sarah Filippi, Mark van der Wilk |
| 2025 | ICML | Rethinking Aleatoric and Epistemic Uncertainty. | Freddie Bickford Smith, Jannik Kossen, Eleanor Trollope, Mark van der Wilk, Adam Foster, Tom Rainforth |
| 2025 | ICST | Turbulence: Systematically and Automatically Testing Instruction-Tuned Large Language Models for Code. | Shahin Honarvar, Mark van der Wilk, Alastair F. Donaldson |
| 2024 | ICML | Bivariate Causal Discovery using Bayesian Model Selection. | Anish Dhir, Samuel Power, Mark van der Wilk |
| 2024 | ICML | Learning in Deep Factor Graphs with Gaussian Belief Propagation. | Seth Nabarro, Mark van der Wilk, Andrew J. Davison |
| 2023 | AISTATS | Actually Sparse Variational Gaussian Processes. | Harry Jake Cunningham, Daniel Augusto de Souza, So Takao, Mark van der Wilk, Marc Peter Deisenroth |
| 2023 | ICML | Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels. | Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk, Gunnar Rtsch, Bernhard Schlkopf |
| 2022 | AISTATS | Last Layer Marginal Likelihood for Invariance Learning. | Pola Schwbel, Martin Jrgensen, Sebastian W. Ober, Mark van der Wilk |
| 2022 | ICLR | Bayesian Neural Network Priors Revisited. | Vincent Fortuin, Adri Garriga-Alonso, Sebastian W. Ober, Florian Wenzel, Gunnar Rtsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison |
| 2022 | UAI | Data augmentation in Bayesian neural networks and the cold posterior effect. | Seth Nabarro, Stoil Ganev, Adri Garriga-Alonso, Vincent Fortuin, Mark van der Wilk, Laurence Aitchison |
| 2022 | UAI | Learning invariant weights in neural networks. | Tycho F. A. van der Ouderaa, Mark van der Wilk |
| 2021 | ICML | Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients. | Artem Artemev, David R. Burt, Mark van der Wilk |
| 2021 | UAI | Correlated weights in infinite limits of deep convolutional neural networks. | Adri Garriga-Alonso, Mark van der Wilk |
| 2021 | UAI | The promises and pitfalls of deep kernel learning. | Sebastian W. Ober, Carl E. Rasmussen, Mark van der Wilk |
| 2020 | AISTATS | Bayesian Image Classification with Deep Convolutional Gaussian Processes. | Vincent Dutordoir, Mark van der Wilk, Artem Artemev, James Hensman |
| 2019 | ICML | Rates of Convergence for Sparse Variational Gaussian Process Regression. | David R. Burt, Carl Edward Rasmussen, Mark van der Wilk |
| 2019 | ICML | Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models. | Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen |
| 2017 | IJCAI | 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 |