mlsanityData to 7 Oct 2026

Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes

arXiv 1706.02524 ↗First posted 8 Jun 2017stat.ML cs.LGcited by 19

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Abstract (arXiv)

Automating statistical modelling is a challenging problem in artificial intelligence. The Automatic Statistician takes a first step in this direction, by employing a kernel search algorithm with Gaussian Processes (GP) to provide interpretable statistical models for regression problems. However this does not scale due to its O(N3) running time for the model selection. We propose Scalable Kernel Composition (SKC), a scalable kernel search algorithm that extends the Automatic Statistician to bigger data sets. In doing so, we derive a cheap upper bound on the GP marginal likelihood that sandwiches the marginal likelihood with the variational lower bound . We show that the upper bound is significantly tighter than the lower bound and thus useful for model selection.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jun 2017.

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Citing papers per year: 2021: 6; 2022: 7; 2023: 3; 2024: 2; 2025: 1.

19 papers.

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  1. 2021Priors in Bayesian Deep Learning: A Reviewcited by 101
  2. 2022Surrogate modeling for Bayesian optimization beyond a single Gaussian processcited by 22
  3. 2021Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradientscited by 20
  4. 2021Incremental Ensemble Gaussian Processescited by 15
  5. 2022Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equationscited by 14
  6. 2021Kernel Identification Through Transformerscited by 13
  7. 2022Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modelingcited by 12
  8. 2023Trace Encoding in Process Mining: a survey and benchmarkingcited by 6
  9. 2023Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian Processescited by 6
  10. 2023Sequential Monte Carlo Learning for Time Series Structure Discoverycited by 5
  11. 2022Gaussian Process Latent Variable Modeling for Few-shot Time Series Forecastingcited by 3
  12. 2025Automated Model Discovery via Multi-modal & Multi-step Pipelinecited by 3
7 more
  1. 2021Model Selection for Production System via Automated Online Experimentscited by 2
  2. 2022Adaptive Cholesky Gaussian Processescited by 2
  3. 2022Weighted Ensembles for Active Learning with Adaptivitycited by 2
  4. 2022Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergencecited by 2
  5. 2024Recommendations for Baselines and Benchmarking Approximate Gaussian Processescited by 2
  6. 2021Online structural kernel selection for mobile healthcited by 0
  7. 2024On the Laplace Approximation as Model Selection Criterion for Gaussian Processescited by 0