mlsanityData to 14 Oct 2026

Memory-Based Dual Gaussian Processes for Sequential Learning

arXiv 2306.03566 ↗First posted 6 Jun 2023cs.LG stat.MLcited by 11

Not on the map. Why?

Abstract (arXiv)

Sequential learning with Gaussian processes (GPs) is challenging when access to past data is limited, for example, in continual and active learning. In such cases, errors can accumulate over time due to inaccuracies in the posterior, hyperparameters, and inducing points, making accurate learning challenging. Here, we present a method to keep all such errors in check using the recently proposed dual sparse variational GP. Our method enables accurate inference for generic likelihoods and improves learning by actively building and updating a memory of past data. We demonstrate its effectiveness in several applications involving Bayesian optimization, active learning, and continual learning.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 16 of its 43 references that are in the map. First posted on arXiv 6 Jun 2023.

Builds on

16 of 43 references.

References · 16

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2013Gaussian Processes for Big Datacited by 383
  3. 2017Variational Continual Learningcited by 339
  4. 2019Functional Regularisation for Continual Learning with Gaussian Processescited by 123
  5. 2020Continual Deep Learning by Functional Regularisation of Memorable Pastcited by 105
  6. 2021The Bayesian Learning Rulecited by 99
  7. 2016The Parallel Knowledge Gradient Method for Batch Bayesian Optimizationcited by 74
  8. 2020Convergence of Sparse Variational Inference in Gaussian Processes Regressioncited by 53
  9. 2017Streaming Sparse Gaussian Process Approximationscited by 41
  10. 2015Modeling and interpolation of the ambient magnetic field by Gaussian processescited by 28
  11. 2020Variational Auto-Regressive Gaussian Processes for Continual Learningcited by 23
  12. 2021Conditioning Sparse Variational Gaussian Processes for Online Decision-makingcited by 23
4 more
  1. 2021Knowledge-Adaptation Priorscited by 20
  2. 2021Dual Parameterization of Sparse Variational Gaussian Processescited by 19
  3. 2022Fantasizing with Dual GPs in Bayesian Optimization and Active Learningcited by 7
  4. 2020BOSH: Bayesian Optimization by Sampling Hierarchicallycited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 4; 2026: 2.

11 papers.

  1. 2024Function-space Parameterization of Neural Networks for Sequential Learningcited by 8
  2. 2025Knowledge Adaptation as Posterior Correctioncited by 6
  3. 2024Preventing Model Collapse in Gaussian Process Latent Variable Modelscited by 4
  4. 2024Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Samplingcited by 3
  5. 2025Recurrent Memory for Online Interdomain Gaussian Processescited by 2
  6. 2023Sparse Function-space Representation of Neural Networkscited by 1
  7. 2024Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?cited by 1
  8. 2026Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discoverycited by 1
  9. 2025Sequential Causal Discovery with Noisy Language Model Priorscited by 0
  10. 2025Novel Pivoted Cholesky Decompositions for Efficient Gaussian Process Inferencecited by 0
  11. 2026Joint Model and Data Sparsification via the Marginal Likelihoodcited by 0