Mark van der Wilk
Associate Professor of Computer Science
Studies generalisation in machine learning through equivariance, Bayesian model selection and Bayesian optimisation.
Mark van der Wilk's lab at Oxford works on probabilistic & causal inference. 25 papers since 2023, 18 at top venues, mostly ICML and NeurIPS. 11 PhD students. 6 alumni. Applications close .
At a glance
- papers, 2023–2026
- 7/ year
- at T1 venues
- 72%
- PhD students
- 11
- MS students
- 0
- alumni listed
- 6
Papers by yearfrom the lab homepage, 2023–2026
- 5 papers2026 · T1 5 · collecting
- 8 papers2025 · T1 5
- 7 papers2024 · T1 5
- 5 papers2023 · T1 3
T1 venuesOtherVenues: ICML, NeurIPS, AISTATS, ICLR, ICRA
Research focuspaper titles that use each topic's words
- Probabilistic & causal inference12 papers
- bayesian9 papers
- model selection2 papers
- generalisation0 papers
- bayesian optimisation0 papers
Paper list
From the lab homepage.
20265 papers
- ICRA (T1)A Distributed Gaussian Process Model for Multi-Robot Mapping (opens in a new tab)
- AISTATS (T1)Inverse-Free Sparse Variational Gaussian Processes (opens in a new tab)
- TMLR (T1)On The Scalability Of Forward Gradients, Evolution Strategies, And Control Variates (opens in a new tab)
- UAI (T1)The relative value of interventional and observational samples in Bayesian Causal Linear Gaussian Models (opens in a new tab)
- ICML (T1)Use What You Know: Causal Foundation Models with Partial Graphs (opens in a new tab)
20258 papers
- ICLR (T1)A Meta-Learning Approach to Bayesian Causal Discovery (opens in a new tab)
- ICML (T1)Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough? (opens in a new tab)
- ICML (T1)Continuous Bayesian Model Selection for Multivariate Causal Discovery (opens in a new tab)
- NeurIPS (T1)Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning (opens in a new tab)
- ICML (T1)Rethinking Aleatoric and Epistemic Uncertainty (opens in a new tab)
- ICML WorkshopSynDaCaTE: A Synthetic Dataset For Evaluating Part-Whole Hierarchical Inference (opens in a new tab)
- Biotechnology and BioengineeringTransfer learning Bayesian optimization for competitor DNA molecule design for use in diagnostic assays (opens in a new tab)
- ICSTTurbulence: Systematically and automatically testing instruction-tuned large language models for code (opens in a new tab)
20247 papers
- ICML (T1)Bivariate Causal Discovery using Bayesian Model Selection (opens in a new tab)
- ICML (T1)Learning in Deep Factor Graphs with Gaussian Belief Propagation (opens in a new tab)
- NeurIPS (T1)Noether' s Razor: Learning Conserved Quantities (opens in a new tab)
- JMLR (T1)Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees (opens in a new tab)
- Imaging NeuroscienceTime-varying functional connectivity as Wishart processes (opens in a new tab)
- NeurIPS (T1)Transition Constrained Bayesian Optimization via Markov Decision Processes (opens in a new tab)
- ICML WorkshopVariational Inference Failures Under Model Symmetries: Permutation Invariant Posteriors for Bayesian Neural Networks (opens in a new tab)
20235 papers
- AISTATS (T1)Actually Sparse Variational Gaussian Processes (opens in a new tab)
- Computers & Chemical EngineeringCombining multi-fidelity modelling and asynchronous batch Bayesian Optimization (opens in a new tab)
- NeurIPS (T1)Learning Layer-wise Equivariances Automatically using Gradients (opens in a new tab)
- NeurIPS WorkshopPractical Path-based Bayesian Optimization (opens in a new tab)
- ICML (T1)Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels (opens in a new tab)
Where alumni went6 listed
Apply to Oxford2027-28
DPhil in Computer Science
- Round
- 2027-28 entry (January round)
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
Similar labsnearest by research summary
- USCMU
Andrea Zanette
- Reinforcement learning
- CNZhejiang
Zhengxing Huang
- Reinforcement learning
- USStanford
Benjamin Van Roy
- Reinforcement learning
- GBUCL
Martin Benning
- Probabilistic & causal inference
- GBCambridge
Damon Wischik
- Probabilistic & causal inference
- GBOxford
Stephen Roberts
- Probabilistic & causal inference
Your notes
Save this lab to keep notes.
Data
Checked against the lab homepage.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Lab homepage
Tiers are venue classifications, not lab rankings.
Oxfordcloses