mlsanityData to 7 Oct 2026

Active Learning of Linear Embeddings for Gaussian Processes

arXiv 1310.6740 ↗First posted 24 Oct 2013stat.ML cs.LGcited by 34

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Abstract (arXiv)

We propose an active learning method for discovering low-dimensional structure in high-dimensional Gaussian process (GP) tasks. Such problems are increasingly frequent and important, but have hitherto presented severe practical difficulties. We further introduce a novel technique for approximately marginalizing GP hyperparameters, yielding marginal predictions robust to hyperparameter mis-specification. Our method offers an efficient means of performing GP regression, quadrature, or Bayesian optimization in high-dimensional spaces.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Oct 2013.

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Citing papers per year: 2021: 5; 2022: 5; 2023: 5; 2024: 8; 2025: 10; 2026: 1.

34 papers.

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  1. 2021High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspacescited by 139
  2. 2024Vanilla Bayesian Optimization Performs Great in High Dimensionscited by 87
  3. 2022Local Latent Space Bayesian Optimization over Structured Inputscited by 78
  4. 2021High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learningcited by 54
  5. 2021LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lassocited by 41
  6. 2023SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian Processescited by 27
  7. 2023Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddingscited by 24
  8. 2024A survey and benchmark of high-dimensional Bayesian optimization of discrete sequencescited by 22
  9. 2022Safe Active Learning for Multi-Output Gaussian Processescited by 13
  10. 2025Posterior Inference with Diffusion Models for High-dimensional Black-box Optimizationcited by 12
  11. 2022Scalable Gaussian-process regression and variable selection using Vecchia approximationscited by 8
  12. 2023High-dimensional mixed-categorical Gaussian processes with application to multidisciplinary design optimization for a green aircraftcited by 8
22 more
  1. 2024"Noisier" Noise Contrastive Eestimation is (Almost) Maximum Likelihoodcited by 8
  2. 2021Global optimization using random embeddingscited by 6
  3. 2025Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft designcited by 6
  4. 2025Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddingscited by 6
  5. 2024High-dimensional Bayesian Optimization via Covariance Matrix Adaptation Strategycited by 5
  6. 2021Sensitivity Prewarping for Local Surrogate Modelingcited by 4
  7. 2023Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary Systemscited by 4
  8. 2022Bayesian Optimization with Informative Covariancecited by 2
  9. 2023Active Cost-aware Labeling of Streaming Datacited by 2
  10. 2025Enhancing Trust-Region Bayesian Optimization via Newton Methodscited by 2
  11. 2025Informed Initialization for Bayesian Optimization and Active Learningcited by 2
  12. 2022Linear Embedding-based High-dimensional Batch Bayesian Optimization without Reconstruction Mappingscited by 1
  13. 2024Combining additivity and active subspaces for high-dimensional Gaussian process modelingcited by 1
  14. 2024Global Optimisation of Black-Box Functions with Generative Models in the Wasserstein Spacecited by 1
  15. 2024Safe Active Learning for Gaussian Differential Equationscited by 1
  16. 2025Leveraging Axis-Aligned Subspaces for High-Dimensional Bayesian Optimization with Group Testingcited by 1
  17. 2024Amortized Active Learning for Nonparametric Functionscited by 0
  18. 2025Sample-Constrained Black Box Optimization for Audio Personalizationcited by 0
  19. 2025Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agentscited by 0
  20. 2025AutoODD: Agentic Audits via Bayesian Red Teaming in Black-Box Modelscited by 0
  21. 2025Nonlinear Dimensionality Reduction Techniques for Bayesian Optimizationcited by 0
  22. 2026Automated Random Embedding for Practical Bayesian Optimization with Unknown Effective Dimensioncited by 0