Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
November 8, 2025Open Access

Adaptive sparse variational approximations for Gaussian process regression

View Full Paper
Ask AI
Bookmark
Share

Authors

DNDennis NiemanBSBotond Szabó

Discussion

Loading...

Member takes

Overview

Theoretical guarantees accompany numerical analysis for hyperparameter selection in variational approximation of Gaussian processes, implying effective generalization.

Key Points

  • Variational approximation enhances hyperparameter selection for effective generalization in regression.
  • Analysis includes results on synthetic and real world datasets, showcasing applicability and performance.
  • The approach derives upper bounds for contraction rates of variational posteriors, establishing theoretical underpinnings.
  • Implications point to the effectiveness of variational Bayes in achieving minimax optimal rates in Gaussian processes.

Cite This Study

Nieman et al. (2025) studied this question.

synapsesocial.com/papers/690e8b6ca5b062d7a4e73648https://doi.org/10.48550/arxiv.2504.03321
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Sparse Variational Student-t Processes2024 · 4 citations
  2. 2Sparse Variational Information Bottleneck Gaussian Processes for Uncertainty Estimation2026 · 1 citations
  3. 3A sparse empirical Bayes approach to high‐dimensional Gaussian process‐based varying coefficient models2024 · 1 citations
  4. 4Loss-Based Variational Bayes Prediction2024 · 3 citations
  5. 5Scalable Variable Selection and Model Averaging for Latent Regression Models Using Approximate Variational Bayes2025