mlsanityData to 7 Oct 2026

Automatic Construction and Natural-Language Description of Nonparametric Regression Models

arXiv 1402.4304 ↗First posted 18 Feb 2014stat.ML cs.LGcited by 40

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Abstract (arXiv)

This paper presents the beginnings of an automatic statistician, focusing on regression problems. Our system explores an open-ended space of statistical models to discover a good explanation of a data set, and then produces a detailed report with figures and natural-language text. Our approach treats unknown regression functions nonparametrically using Gaussian processes, which has two important consequences. First, Gaussian processes can model functions in terms of high-level properties (e.g. smoothness, trends, periodicity, changepoints). Taken together with the compositional structure of our language of models this allows us to automatically describe functions in simple terms. Second, the use of flexible nonparametric models and a rich language for composing them in an open-ended manner also results in state-of-the-art extrapolation performance evaluated over 13 real time series data sets from various domains.

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Feb 2014.

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Citing papers per year: 2021: 8; 2022: 9; 2023: 8; 2024: 4; 2025: 5; 2026: 6.

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  1. 2021Priors in Bayesian Deep Learning: A Reviewcited by 101
  2. 2023The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learningcited by 63
  3. 2022Bayesian Model Selection, the Marginal Likelihood, and Generalizationcited by 56
  4. 2024Automated Statistical Model Discovery with Language Modelscited by 41
  5. 2023A Primer on Bayesian Neural Networks: Review and Debatescited by 26
  6. 2021Automating Data Science: Prospects and Challengescited by 17
  7. 2025Adaptive Kernel Design for Bayesian Optimization Is a Piece of CAKE with LLMscited by 15
  8. 2021Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detectioncited by 13
  9. 2025Generating Computational Cognitive Models using Large Language Modelscited by 12
  10. 2023Amortized Inference for Gaussian Process Hyperparameters of Structured Kernelscited by 10
  11. 2022Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transportcited by 8
  12. 2022Tisane: Authoring Statistical Models via Formal Reasoning from Conceptual and Data Relationshipscited by 5
28 more
  1. 2023Sequential Monte Carlo Learning for Time Series Structure Discoverycited by 5
  2. 2022Online Time Series Anomaly Detection with State Space Gaussian Processescited by 4
  3. 2023Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary Systemscited by 4
  4. 2021The Minecraft Kernel: Modelling correlated Gaussian Processes in the Fourier domaincited by 3
  5. 2022Gaussian Process Latent Variable Modeling for Few-shot Time Series Forecastingcited by 3
  6. 2025Automated Model Discovery via Multi-modal & Multi-step Pipelinecited by 3
  7. 2021Model Selection for Production System via Automated Online Experimentscited by 2
  8. 2023On the Identifiability and Interpretability of Gaussian Process Modelscited by 2
  9. 2025Just One Layer Norm Guarantees Stable Extrapolationcited by 2
  10. 2021Gaussian Process Latent Class Choice Modelscited by 1
  11. 2021Tractable Computation of Expected Kernelscited by 1
  12. 2022Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processescited by 1
  13. 2022Trust in Language Grounding: a new AI challenge for human-robot teamscited by 1
  14. 2023Beyond Intuition, a Framework for Applying GPs to Real-World Datacited by 1
  15. 2023Learning material synthesis-process-structure-property relationship by data fusion: Bayesian Coregionalization N-Dimensional Piecewise Function Learningcited by 1
  16. 2024Exploratory Visual Analysis for Increasing Data Readiness in Artificial Intelligence Projectscited by 1
  17. 2026VESTA: Visual Exploration with Statistical Tool Agentscited by 1
  18. 2021Online structural kernel selection for mobile healthcited by 0
  19. 2022A Change Dynamic Model for the Online Detection of Gradual Changecited by 0
  20. 2022A Survey of Open Source Automation Tools for Data Science Predictionscited by 0
  21. 2024Uncertainty-Aware Calibration of a Hot-Wire Anemometer With Gaussian Process Regressioncited by 0
  22. 2024On the Laplace Approximation as Model Selection Criterion for Gaussian Processescited by 0
  23. 2025Scalable Deep Basis Kernel Gaussian Processescited by 0
  24. 2026Mechanistic Evidence for Spectral Structures in Prior-Data Fitted Networkscited by 0
  25. 2026Bayesian Nonparametric Detection of Anomalies in Multivariate Functional Datacited by 0
  26. 2026Covariance-Boosted Gaussian Processes for Spatiotemporal Irregularitiescited by 0
  27. 2026Why not to use the Gaussian kernelcited by 0
  28. 2026LineupRL: Verifiable Reinforcement Learning for Time Series Captioning via Caption-to-Series Identificationcited by 0