Mark van der Wilk
39 papers · 2014–2026 · 7 venues · across top CS/AI venues
This profile combines several different researchers. Authors are keyed by name, and this name's papers fall into 10 separate collaboration groups - the largest holds only 38% of them. Pick the group below that matches the researcher you mean. How a shared name is split.
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Which Mark van der Wilk?
4 groups of papers that share co-authors internally but none with each other - each most likely a different researcher. Pick one to see its papers.
with Carl Edward Rasmussen, James Hensman, Yarin Gal
15 papers
· 2014–2024
· NeurIPS, JMLR, ICML
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Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
JMLR 2024
Memory safe computations with XLA compiler
NeurIPS 2022
Deep Neural Networks as Point Estimates for Deep Gaussian Processes
NeurIPS 2021
Speedy Performance Estimation for Neural Architecture Search
NeurIPS 2021
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients
ICML 2021
A Bayesian Perspective on Training Speed and Model Selection
NeurIPS 2020
Convergence of Sparse Variational Inference in Gaussian Processes Regression
JMLR 2020
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models
ICML 2019
Rates of Convergence for Sparse Variational Gaussian Process Regression
ICML 2019
Learning Invariances using the Marginal Likelihood
NeurIPS 2018
Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning
IJCAI 2017
Convolutional Gaussian Processes
NeurIPS 2017
GPflow: A Gaussian Process Library using TensorFlow
JMLR 2017
Understanding Probabilistic Sparse Gaussian Process Approximations
NeurIPS 2016
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models
NeurIPS 2014
6 papers whose co-authors appear nowhere else under this name
Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?
ICML 2025
Rethinking Aleatoric and Epistemic Uncertainty
ICML 2025
Actually Sparse Variational Gaussian Processes
AISTATS 2023
Learning invariant weights in neural networks
UAI 2022
Bayesian Layers: A Module for Neural Network Uncertainty
NeurIPS 2019
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes
NeurIPS 2019