mlsanityData to 7 Oct 2026

Fantasizing with Dual GPs in Bayesian Optimization and Active Learning

arXiv 2211.01053 ↗First posted 2 Nov 2022cs.LG stat.MLcited by 7

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Abstract (arXiv)

Gaussian processes (GPs) are the main surrogate functions used for sequential modelling such as Bayesian Optimization and Active Learning. Their drawbacks are poor scaling with data and the need to run an optimization loop when using a non-Gaussian likelihood. In this paper, we focus on `fantasizing' batch acquisition functions that need the ability to condition on new fantasized data computationally efficiently. By using a sparse Dual GP parameterization, we gain linear scaling with batch size as well as one-step updates for non-Gaussian likelihoods, thus extending sparse models to greedy batch fantasizing acquisition functions.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 16 of its 23 references that are in the map. First posted on arXiv 2 Nov 2022.

Builds on

16 of 23 references.

References · 16

  1. 2014Scalable Variational Gaussian Process Classificationcited by 226
  2. 2018Maximizing acquisition functions for Bayesian optimizationcited by 113
  3. 2021The Bayesian Learning Rulecited by 95
  4. 2016The Parallel Knowledge Gradient Method for Batch Bayesian Optimizationcited by 73
  5. 2017Conjugate-Computation Variational Inference : Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Modelscited by 65
  6. 2021GIBBON: General-purpose Information-Based Bayesian OptimisatioNcited by 47
  7. 2017Streaming Sparse Gaussian Process Approximationscited by 41
  8. 2020Scalable Thompson Sampling using Sparse Gaussian Process Modelscited by 39
  9. 2021Conditioning Sparse Variational Gaussian Processes for Online Decision-makingcited by 23
  10. 2013Multiobjective optimization using Gaussian process emulators via stepwise uncertainty reductioncited by 21
  11. 2020Fast Variational Learning in State-Space Gaussian Process Modelscited by 20
  12. 2021Dual Parameterization of Sparse Variational Gaussian Processescited by 19
4 more
  1. 2020Bayesian Quantile and Expectile Optimisationcited by 17
  2. 2020BOSH: Bayesian Optimization by Sampling Hierarchicallycited by 6
  3. 2022Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisationcited by 5
  4. 2022A penalisation method for batch multi-objective Bayesian optimisation with application in heat exchanger designcited by 5

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 4; 2024: 0; 2025: 2.

7 papers.

  1. 2022GAUCHE: A Library for Gaussian Processes in Chemistrycited by 40
  2. 2023Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisationcited by 32
  3. 2025Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spacescited by 13
  4. 2023Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlowcited by 11
  5. 2023Memory-Based Dual Gaussian Processes for Sequential Learningcited by 11
  6. 2023MONGOOSE: Path-wise Smooth Bayesian Optimisation via Meta-learningcited by 5
  7. 2025In-Context Multi-Objective Optimizationcited by 5