mlsanityData to 14 Oct 2026

Adaptive Inducing Points Selection For Gaussian Processes

arXiv 2107.10066 ↗First posted 21 Jul 2021stat.ML cs.LGcited by 8

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Abstract (arXiv)

Gaussian Processes (GPs) are flexible non-parametric models with strong probabilistic interpretation. While being a standard choice for performing inference on time series, GPs have few techniques to work in a streaming setting. citebui2017streaming developed an efficient variational approach to train online GPs by using sparsity techniques: The whole set of observations is approximated by a smaller set of inducing points (IPs) and moved around with new data. Both the number and the locations of the IPs will affect greatly the performance of the algorithm. In addition to optimizing their locations, we propose to adaptively add new points, based on the properties of the GP and the structure of the data.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 8 of its 17 references that are in the map. First posted on arXiv 21 Jul 2021.

Builds on

8 of 17 references.

References · 8

  1. 2016Variational Fourier features for Gaussian processescited by 85
  2. 2016Understanding Probabilistic Sparse Gaussian Process Approximationscited by 68
  3. 2019Rates of Convergence for Sparse Variational Gaussian Process Regressioncited by 63
  4. 2017Streaming Sparse Gaussian Process Approximationscited by 41
  5. 2017Non-Stationary Spectral Kernelscited by 38
  6. 2018Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentationcited by 17
  7. 2019Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentationcited by 16
  8. 2019Continual Multi-task Gaussian Processescited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 2; 2025: 1; 2026: 1.

8 papers.

  1. 2022Latent Graph Inference using Product Manifoldscited by 23
  2. 2021Recipes for when Physics Fails: Recovering Robust Learning of Physics Informed Neural Networkscited by 18
  3. 2024Approximation-Aware Bayesian Optimizationcited by 8
  4. 2022Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Treescited by 5
  5. 2023REDUCR: Robust Data Downsampling Using Class Priority Reweightingcited by 3
  6. 2026Fluids You Can Trust: Property-Preserving Operator Learning for Incompressible Flowscited by 3
  7. 2024Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?cited by 1
  8. 2025Atlas Gaussian processes on restricted domains and point cloudscited by 0