mlsanityData to 7 Oct 2026

Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation

arXiv 2301.10123 ↗First posted 24 Jan 2023cs.LG stat.MLcited by 32

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Abstract (arXiv)

Sparse Gaussian Processes are a key component of high-throughput Bayesian Optimisation (BO) loops; however, we show that existing methods for allocating their inducing points severely hamper optimisation performance. By exploiting the quality-diversity decomposition of Determinantal Point Processes, we propose the first inducing point allocation strategy designed specifically for use in BO. Unlike existing methods which seek only to reduce global uncertainty in the objective function, our approach provides the local high-fidelity modelling of promising regions required for precise optimisation. More generally, we demonstrate that our proposed framework provides a flexible way to allocate modelling capacity in sparse models and so is suitable broad range of downstream sequential decision making tasks.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). Builds on 40 of its 73 references that are in the map. First posted on arXiv 24 Jan 2023.

Builds on

40 of 73 references.

References · 40

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  2. 2019Scalable Global Optimization via Local Bayesian Optimizationcited by 359
  3. 2014Scalable Variational Gaussian Process Classificationcited by 226
  4. 2017Max-value Entropy Search for Efficient Bayesian Optimizationcited by 202
  5. 2014Predictive Entropy Search for Efficient Global Optimization of Black-box Functionscited by 201
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28 more
  1. 2020BOSS: Bayesian Optimization over String Spacescited by 54
  2. 2020Convergence of Sparse Variational Inference in Gaussian Processes Regressioncited by 53
  3. 2017Batched High-dimensional Bayesian Optimization via Structural Kernel Learningcited by 51
  4. 2022Constrained Efficient Global Optimization of Expensive Black-box Functionscited by 49
  5. 2019Deep Gaussian Processes for Multi-fidelity Modelingcited by 48
  6. 2021GIBBON: General-purpose Information-Based Bayesian OptimisatioNcited by 47
  7. 2022GAUCHE: A Library for Gaussian Processes in Chemistrycited by 40
  8. 2020Scalable Thompson Sampling using Sparse Gaussian Process Modelscited by 39
  9. 2016Batched Gaussian Process Bandit Optimization via Determinantal Point Processescited by 33
  10. 2021TREGO: a Trust-Region Framework for Efficient Global Optimizationcited by 28
  11. 2021Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processescited by 23
  12. 2021Conditioning Sparse Variational Gaussian Processes for Online Decision-makingcited by 23
  13. 2020MUMBO: MUlti-task Max-value Bayesian Optimizationcited by 21
  14. 2017Fast Information-theoretic Bayesian Optimisationcited by 19
  15. 2019Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its parallelizationcited by 19
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 11; 2025: 10; 2026: 6.

32 papers.

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  2. 2025Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spacescited by 13
  3. 2023Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlowcited by 11
  4. 2024COMO: Compact Mapping and Odometrycited by 10
  5. 2025We Still Don't Understand High-Dimensional Bayesian Optimizationcited by 9
  6. 2023Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimationcited by 8
  7. 2023Sparse Variational Student-t Processescited by 8
  8. 2024Approximation-Aware Bayesian Optimizationcited by 8
  9. 2024Scalable Bayesian Optimization via Focalized Sparse Gaussian Processescited by 8
  10. 2024This Too Shall Pass: Removing Stale Observations in Dynamic Bayesian Optimizationcited by 6
  11. 2024Bayesian Optimisation with Unknown Hyperparameters: Regret Bounds Logarithmically Closer to Optimalcited by 6
  12. 2025Koopman-Equivariant Gaussian Processescited by 6
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  1. 2023MONGOOSE: Path-wise Smooth Bayesian Optimisation via Meta-learningcited by 5
  2. 2023Fast post-process Bayesian inference with Variational Sparse Bayesian Quadraturecited by 3
  3. 2024No-Regret Learning of Nash Equilibrium for Black-Box Games via Gaussian Processescited by 3
  4. 2025Amortized Safe Active Learning for Real-Time Data Acquisition: Pretrained Neural Policies From Simulated Nonparametric Functionscited by 3
  5. 2024Motion Code: Robust Time Series Classification and Forecasting via Sparse Variational Multi-Stochastic Processes Learningcited by 2
  6. 2024Sparse Variational Student-t Processes for Heavy-tailed Modelingcited by 2
  7. 2024Combining additivity and active subspaces for high-dimensional Gaussian process modelingcited by 1
  8. 2024Global Safe Sequential Learning via Efficient Knowledge Transfercited by 1
  9. 2025Robust and Computation-Aware Gaussian Processescited by 1
  10. 2024SCORE: A 1D Reparameterization Technique to Break Bayesian Optimization's Curse of Dimensionalitycited by 0
  11. 2025Gradient-based Sample Selection for Faster Bayesian Optimizationcited by 0
  12. 2025Adaptive Replication Strategies in Trust-Region-Based Bayesian Optimization of Stochastic Functionscited by 0
  13. 2025Taking the GP Out of the Loopcited by 0
  14. 2025Atlas Gaussian processes on restricted domains and point cloudscited by 0
  15. 2026Joint Parameter and State-Space Bayesian Optimization: Using Process Expertise to Accelerate Manufacturing Optimizationcited by 0
  16. 2026ORTHOBO: Orthogonal Bayesian Hyperparameter Optimizationcited by 0
  17. 2026B3O: Scalable Boltzmann Batch Bayesian Optimizationcited by 0
  18. 2026Sequential sparse Gaussian process quantile regressioncited by 0
  19. 2026On Basis Function Selection for Sparse Gaussian Process Regressioncited by 0
  20. 2026Bayesian Optimization with Fisher Information Geometry: Gradient Bounds and Trust-Region Methodscited by 0