← Authors

Mark van der Wilk

38 papers · 2014–2025 · 7 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (7) πŸƒ Academic Marathon (11) 🐝 Cross-Pollinator (13) πŸ—ΊοΈ Taxonomy Completionist (41) πŸ”¬ Deep Specialist (20) πŸ† Keyword Champion (8) πŸ—ƒοΈ Keyword Collector (109) ⚑ Prolific Year (5) πŸ’Ž Century Club (38) ❓ The Questioner πŸ”₯ Unstoppable (10)

Conferences

NIPS (17) ICML (9) UAI (4) JMLR (3) AISTATS (2) ICLR (2) IJCAI (1)

Top co-authors

Carl Edward Rasmussen (7) James Hensman (5) Yarin Gal (4) Vincent Fortuin (3) Anish Dhir (3) Sebastian W. Ober (3) Gunnar RΓ€tsch (3) David R. Burt (3) AdriΓ  Garriga-Alonso (3) Tycho van der Ouderaa (3)

Research topics

Optimization & Theory (1)

Keywords

gaussian process (14) variational inference (14) marginal likelihood (8) data augmentation (4) sparse approximation (4) inducing variable (3) sparse gaussian process (3) bayesian model selection (3) laplace approximation (3) neural network (3) uncertainty quantification (3) invariance learning (3) convolutional neural network (3) inducing point (2) bayesian inference (2) black-box optimization (2) bayesian neural network (2) bayesian optimization (2) dynamical system (2) image classification (2)

Papers

Continuous Bayesian Model Selection for Multivariate Causal Discovery ICML 2025
Rethinking Aleatoric and Epistemic Uncertainty ICML 2025
A Meta-Learning Approach to Bayesian Causal Discovery ICLR 2025
Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough? ICML 2025
Transition Constrained Bayesian Optimization via Markov Decision Processes NIPS 2024
Learning in Deep Factor Graphs with Gaussian Belief Propagation ICML 2024
Bivariate Causal Discovery using Bayesian Model Selection ICML 2024
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees JMLR 2024
Noether's Razor: Learning Conserved Quantities NIPS 2024
Actually Sparse Variational Gaussian Processes AISTATS 2023
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels ICML 2023
Learning Layer-wise Equivariances Automatically using Gradients NIPS 2023
Learning invariant weights in neural networks UAI 2022
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations NIPS 2022
Memory safe computations with XLA compiler NIPS 2022
Relaxing Equivariance Constraints with Non-stationary Continuous Filters NIPS 2022
SnAKe: Bayesian Optimization with Pathwise Exploration NIPS 2022
Last Layer Marginal Likelihood for Invariance Learning AISTATS 2022
Bayesian Neural Network Priors Revisited ICLR 2022
Data augmentation in Bayesian neural networks and the cold posterior effect UAI 2022
Correlated weights in infinite limits of deep convolutional neural networks UAI 2021
Deep Neural Networks as Point Estimates for Deep Gaussian Processes NIPS 2021
Speedy Performance Estimation for Neural Architecture Search NIPS 2021
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients ICML 2021
The promises and pitfalls of deep kernel learning UAI 2021
Convergence of Sparse Variational Inference in Gaussian Processes Regression JMLR 2020
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty NIPS 2020
A Bayesian Perspective on Training Speed and Model Selection NIPS 2020
Bayesian Layers: A Module for Neural Network Uncertainty NIPS 2019
Rates of Convergence for Sparse Variational Gaussian Process Regression ICML 2019
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models ICML 2019
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes NIPS 2019
Learning Invariances using the Marginal Likelihood NIPS 2018
Convolutional Gaussian Processes NIPS 2017
Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning IJCAI 2017
GPflow: A Gaussian Process Library using TensorFlow JMLR 2017
Understanding Probabilistic Sparse Gaussian Process Approximations NIPS 2016
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models NIPS 2014