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Batch Active Learning in Gaussian Process Regression using Derivatives

arXiv 2408.01861 ↗First posted 3 Aug 2024cs.LG stat.MLcited by 0

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Abstract (arXiv)

We investigate the use of derivative information for Batch Active Learning in Gaussian Process regression models. The proposed approach employs the predictive covariance matrix for selection of data batches to exploit full correlation of samples. We theoretically analyse our proposed algorithm taking different optimality criteria into consideration and provide empirical comparisons highlighting the advantage of incorporating derivatives information. Our results show the effectiveness of our approach across diverse applications.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 56 references that are in the map. First posted on arXiv 3 Aug 2024.

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17 of 56 references.

References · 17

  1. 2018GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Accelerationcited by 609
  2. 2019BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learningcited by 432
  3. 2017Machine Learning of Linear Differential Equations using Gaussian Processescited by 140
  4. 2019Bayesian Batch Active Learning as Sparse Subset Approximationcited by 68
  5. 2017Bayesian Optimization with Gradientscited by 59
  6. 2017Scalable Log Determinants for Gaussian Process Kernel Learningcited by 35
  7. 2018Scaling Gaussian Process Regression with Derivativescited by 27
  8. 2020Stopping criterion for active learning based on deterministic generalization boundscited by 20
  9. 2015Large-scale Log-determinant Computation through Stochastic Chebyshev Expansionscited by 17
  10. 2022Safe Active Learning for Multi-Output Gaussian Processescited by 13
  11. 2024Safe Active Learning for Time-Series Modeling with Gaussian Processescited by 12
  12. 2023Amortized Inference for Gaussian Process Hyperparameters of Structured Kernelscited by 10
5 more
  1. 2017Exploiting gradients and Hessians in Bayesian optimization and Bayesian quadraturecited by 9
  2. 2022Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transportcited by 8
  3. 2017Bayesian Inference of Log Determinantscited by 4
  4. 2018Offline EEG-Based Driver Drowsiness Estimation Using Enhanced Batch-Mode Active Learning (EBMAL) for Regressioncited by 2
  5. 2021Active Learning in Gaussian Process State Space Modelcited by 2

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