mlsanityData to 14 Oct 2026

Conditioning Sparse Variational Gaussian Processes for Online Decision-making

arXiv 2110.15172 ↗First posted 28 Oct 2021cs.LG stat.MLcited by 23

Not on the map. Why?

Abstract (arXiv)

With a principled representation of uncertainty and closed form posterior updates, Gaussian processes (GPs) are a natural choice for online decision making. However, Gaussian processes typically require at least O(n2) computations for n training points, limiting their general applicability. Stochastic variational Gaussian processes (SVGPs) can provide scalable inference for a dataset of fixed size, but are difficult to efficiently condition on new data. We propose online variational conditioning (OVC), a procedure for efficiently conditioning SVGPs in an online setting that does not require re-training through the evidence lower bound with the addition of new data. OVC enables the pairing of SVGPs with advanced look-ahead acquisition functions for black-box optimization, even with non-Gaussian likelihoods. We show OVC provides compelling performance in a range of applications including active learning of malaria incidence, and reinforcement learning on MuJoCo simulated robotic control tasks.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). Builds on 37 of its 89 references that are in the map. First posted on arXiv 28 Oct 2021.

Builds on

37 of 89 references.

References · 37

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2016OpenAI Gymcited by 1,736
  3. 2016Automatic chemical design using a data-driven continuous representation of moleculescited by 765
  4. 2018A Tutorial on Bayesian Optimizationcited by 744
  5. 2018GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Accelerationcited by 609
  6. 2013Gaussian Processes for Big Datacited by 383
  7. 2019Scalable Global Optimization via Local Bayesian Optimizationcited by 374
  8. 2014Scalable Variational Gaussian Process Classificationcited by 228
  9. 2017Max-value Entropy Search for Efficient Bayesian Optimizationcited by 206
  10. 2015Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)cited by 177
  11. 2021High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspacescited by 141
  12. 2019Functional Regularisation for Continual Learning with Gaussian Processescited by 123
25 more
  1. 2020Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retrainingcited by 123
  2. 2017Batched Large-scale Bayesian Optimization in High-dimensional Spacescited by 117
  3. 2020Continual Deep Learning by Functional Regularisation of Memorable Pastcited by 105
  4. 2017Constrained Bayesian Optimization with Noisy Experimentscited by 104
  5. 2020Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Searchcited by 89
  6. 2016The Parallel Knowledge Gradient Method for Batch Bayesian Optimizationcited by 74
  7. 2016Understanding Probabilistic Sparse Gaussian Process Approximationscited by 68
  8. 2017Conjugate-Computation Variational Inference : Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Modelscited by 65
  9. 2019Rates of Convergence for Sparse Variational Gaussian Process Regressioncited by 63
  10. 2019Deep Gaussian Processes for Multi-fidelity Modelingcited by 50
  11. 2013Streaming Variational Bayescited by 48
  12. 2018Constant-Time Predictive Distributions for Gaussian Processescited by 48
  13. 2021GIBBON: General-purpose Information-Based Bayesian OptimisatioNcited by 47
  14. 2018Meta Reinforcement Learning with Latent Variable Gaussian Processescited by 44
  15. 2017Streaming Sparse Gaussian Process Approximationscited by 41
  16. 2020Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Treescited by 41
  17. 2020Variational Auto-Regressive Gaussian Processes for Continual Learningcited by 23
  18. 2019Bayesian Optimization using Deep Gaussian Processescited by 22
  19. 2017GPflowOpt: A Bayesian Optimization Library using TensorFlowcited by 18
  20. 2021Kernel Interpolation for Scalable Online Gaussian Processescited by 18
  21. 2015Gaussian Process Planning with Lipschitz Continuous Reward Functions: Towards Unifying Bayesian Optimization, Active Learning, and Beyondcited by 15
  22. 2019Continual Multi-task Gaussian Processescited by 7
  23. 2014GP-Localize: Persistent Mobile Robot Localization using Online Sparse Gaussian Process Observation Modelcited by 6
  24. 2018Fully Scalable Gaussian Processes using Subspace Inducing Inputscited by 4
  25. 2020Recyclable Gaussian Processescited by 1

Built on by

Cited by year

Citing papers per year: 2022: 9; 2023: 6; 2024: 4; 2025: 4.

23 papers.

Sort:

  1. 2022Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoderscited by 102
  2. 2022Multi-Objective GFlowNetscited by 101
  3. 2022GAUCHE: A Library for Gaussian Processes in Chemistrycited by 41
  4. 2023Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisationcited by 32
  5. 2022Bayesian Optimization with Conformal Prediction Setscited by 28
  6. 2023Multi-Fidelity Active Learning with GFlowNetscited by 15
  7. 2022Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processescited by 11
  8. 2023Memory-Based Dual Gaussian Processes for Sequential Learningcited by 11
  9. 2024Approximation-Aware Bayesian Optimizationcited by 8
  10. 2024Scalable Bayesian Optimization via Focalized Sparse Gaussian Processescited by 8
  11. 2022Fantasizing with Dual GPs in Bayesian Optimization and Active Learningcited by 7
  12. 2024Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitionscited by 7
11 more
  1. 2023Fast post-process Bayesian inference with Variational Sparse Bayesian Quadraturecited by 3
  2. 2022Weighted Ensembles for Active Learning with Adaptivitycited by 2
  3. 2023BatchGFN: Generative Flow Networks for Batch Active Learningcited by 2
  4. 2025Recurrent Memory for Online Interdomain Gaussian Processescited by 2
  5. 2025Robust, Online, and Adaptive Decentralized Gaussian Processescited by 2
  6. 2022Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processescited by 1
  7. 2024Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?cited by 1
  8. 2025Robust and Computation-Aware Gaussian Processescited by 1
  9. 2022Streaming PAC-Bayes Gaussian process regression with a performance guarantee for online decision makingcited by 0
  10. 2023Exact and general decoupled solutions of the LMC Multitask Gaussian Process modelcited by 0
  11. 2025Fast Gaussian process inference by exact Mat'ern kernel decompositioncited by 0