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Mark van der Wilk

Identifiers

  • name variant Mark van der Wilk 0.60 · backfill

Papers (64)

  1. The Neural Tangent Kernel for Classification cs.LG · 2026 · author #5
  2. Meta-learning for sample-efficient Bayesian optimisation of fed-batch processes math.OC · 2026 · author #8
  3. Symmetry Guarantees Statistic Recovery in Variational Inference stat.ML · 2026 · author #3
  4. Use What You Know: Causal Foundation Models with Partial Graphs cs.LG · 2026 · author #8
  5. SynDaCaTE: A Synthetic Dataset For Evaluating Part-Whole Hierarchical Inference cs.CV · 2025 · author #2
  6. PSyDUCK: Training-Free Steganography for Latent Diffusion cs.LG · 2025 · author #3
  7. Rethinking Aleatoric and Epistemic Uncertainty cs.LG · 2024 · author #4
  8. A Meta-Learning Approach to Bayesian Causal Discovery cs.LG · 2024 · author #4
  9. Continuous Bayesian Model Selection for Multivariate Causal Discovery stat.ML · 2024 · author #5
  10. Noether's razor: Learning Conserved Quantities cs.LG · 2024 · author #2
  11. Variational Inference Failures Under Model Symmetries: Permutation Invariant Posteriors for Bayesian Neural Networks cs.LG · 2024 · author #3
  12. System-Aware Neural ODE Processes for Few-Shot Bayesian Optimization cs.LG · 2024 · author #5
  13. Transfer Learning Bayesian Optimization to Design Competitor DNA Molecules for Use in Diagnostic Assays q-bio.QM · 2024 · author #5
  14. Recommendations for Baselines and Benchmarking Approximate Gaussian Processes cs.LG · 2024 · author #5
  15. Transition Constrained Bayesian Optimization via Markov Decision Processes cs.LG · 2024 · author #7
  16. Turbulence: Systematically and Automatically Testing Instruction-Tuned Large Language Models for Code cs.SE · 2023 · author #2
  17. Practical Path-based Bayesian Optimization cs.LG · 2023 · author #8
  18. Learning in Deep Factor Graphs with Gaussian Belief Propagation cs.LG · 2023 · author #2
  19. Learning Layer-wise Equivariances Automatically using Gradients cs.LG · 2023 · author #3
  20. Current Methods for Drug Property Prediction in the Real World q-bio.BM · 2023 · author #5
  21. Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels stat.ML · 2023 · author #3
  22. Bivariate Causal Discovery using Bayesian Model Selection stat.ML · 2023 · author #3
  23. Actually Sparse Variational Gaussian Processes stat.ML · 2023 · author #4
  24. Combining Multi-Fidelity Modelling and Asynchronous Batch Bayesian Optimization cs.LG · 2022 · author #6
  25. Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees stat.ML · 2022 · author #5
  26. Memory Safe Computations with XLA Compiler cs.LG · 2022 · author #3
  27. Relaxing Equivariance Constraints with Non-stationary Continuous Filters cs.LG · 2022 · author #3
  28. Learning Invariant Weights in Neural Networks stat.ML · 2022 · author #2
  29. Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations stat.ML · 2022 · author #5
  30. SnAKe: Bayesian Optimization with Pathwise Exploration cs.LG · 2022 · author #7
  31. Barely Biased Learning for Gaussian Process Regression stat.ML · 2021 · author #3
  32. A Bayesian Approach to Invariant Deep Neural Networks stat.ML · 2021 · author #4
  33. Last Layer Marginal Likelihood for Invariance Learning stat.ML · 2021 · author #4
  34. Data augmentation in Bayesian neural networks and the cold posterior effect stat.ML · 2021 · author #5
  35. BNNpriors: A library for Bayesian neural network inference with different prior distributions stat.ML · 2021 · author #3
  36. Deep Neural Networks as Point Estimates for Deep Gaussian Processes stat.ML · 2021 · author #3
  37. GPflux: A Library for Deep Gaussian Processes stat.ML · 2021 · author #7
  38. The Promises and Pitfalls of Deep Kernel Learning stat.ML · 2021 · author #3
  39. Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients stat.ML · 2021 · author #3
  40. Bayesian Neural Network Priors Revisited stat.ML · 2021 · author #7
  41. Correlated Weights in Infinite Limits of Deep Convolutional Neural Networks stat.ML · 2021 · author #2
  42. Design of Experiments for Verifying Biomolecular Networks q-bio.QM · 2020 · author #5
  43. Understanding Variational Inference in Function-Space stat.ML · 2020 · author #4
  44. A Bayesian Perspective on Training Speed and Model Selection cs.LG · 2020 · author #5
  45. Convergence of Sparse Variational Inference in Gaussian Processes Regression stat.ML · 2020 · author #3
  46. Variational Orthogonal Features stat.ML · 2020 · author #3
  47. Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty cs.CV · 2020 · author #7
  48. Speedy Performance Estimation for Neural Architecture Search stat.ML · 2020 · author #5
  49. On the Benefits of Invariance in Neural Networks cs.LG · 2020 · author #2
  50. Capsule Networks -- A Probabilistic Perspective cs.LG · 2020 · author #4
  51. A Framework for Interdomain and Multioutput Gaussian Processes stat.ML · 2020 · author #1
  52. Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes stat.ML · 2019 · author #2
  53. Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models stat.ML · 2019 · author #2
  54. Rates of Convergence for Sparse Variational Gaussian Process Regression stat.ML · 2019 · author #3
  55. Bayesian Image Classification with Deep Convolutional Gaussian Processes stat.ML · 2019 · author #2
  56. Non-Factorised Variational Inference in Dynamical Systems stat.ML · 2018 · author #2
  57. Bayesian Layers: A Module for Neural Network Uncertainty cs.LG · 2018 · author #3
  58. Closed-form Inference and Prediction in Gaussian Process State-Space Models stat.ML · 2018 · author #2
  59. Learning Invariances using the Marginal Likelihood cs.LG · 2018 · author #1
  60. Convolutional Gaussian Processes stat.ML · 2017 · author #1
  61. GPflow: A Gaussian process library using TensorFlow stat.ML · 2016 · author #2
  62. Understanding Probabilistic Sparse Gaussian Process Approximations stat.ML · 2016 · author #2
  63. Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models - a Gentle Tutorial stat.ML · 2014 · author #2
  64. Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models stat.ML · 2014 · author #2

Mentions

  • 2412.20892 #4 · arxiv_oai · confidence 0.70 Mark van der Wilk
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  • 2004.03553 #4 · arxiv_oai · confidence 0.70 Mark van der Wilk
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  • 2008.00323 #3 · arxiv_oai · confidence 0.70 Mark van der Wilk
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  • 2605.17606 #5 · arxiv_oai · confidence 0.70 Mark van der Wilk

Frequent Coauthors