mlsanityData to 7 Oct 2026

Lightweight Distributed Gaussian Process Regression for Online Machine Learning

arXiv 2105.04738 ↗First posted 11 May 2021cs.LG cs.MAcited by 4

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Abstract (arXiv)

In this paper, we study the problem where a group of agents aim to collaboratively learn a common static latent function through streaming data. We propose a lightweight distributed Gaussian process regression (GPR) algorithm that is cognizant of agents' limited capabilities in communication, computation and memory. Each agent independently runs agent-based GPR using local streaming data to predict test points of interest; then the agents collaboratively execute distributed GPR to obtain global predictions over a common sparse set of test points; finally, each agent fuses results from distributed GPR with agent-based GPR to refine its predictions. By quantifying the transient and steady-state performances in predictive variance and error, we show that limited inter-agent communication improves learning performances in the sense of Pareto. Monte Carlo simulation is conducted to evaluate the developed algorithm.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 11 of its 55 references that are in the map. First posted on arXiv 11 May 2021.

Builds on

11 of 55 references.

References · 11

  1. 2017Wild Patterns: Ten Years After the Rise of Adversarial Machine Learningcited by 411
  2. 2018When Gaussian Process Meets Big Data: A Review of Scalable GPscited by 205
  3. 2014Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasetscited by 71
  4. 2019Rates of Convergence for Sparse Variational Gaussian Process Regressioncited by 62
  5. 2017Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNscited by 55
  6. 2018Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regressioncited by 33
  7. 2017Safe Learning of Quadrotor Dynamics Using Barrier Certificatescited by 32
  8. 2017Learning of Gaussian Processes in Distributed and Communication Limited Systemscited by 5
  9. 2016Online semi-parametric learning for inverse dynamics modelingcited by 4
  10. 2020Data-Driven Distributed State Estimation and Behavior Modeling in Sensor Networkscited by 2
  11. 2013Anytime computation algorithms for approach-evasion differential gamescited by 1

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Cited by year (2026 to 7 Oct)

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

4 papers.

  1. 2025Streaming Generated Gaussian Process Experts for Online Learning and Control: Extended Versioncited by 4
  2. 2026Quality or Quantity? Error-Informed Selective Online Learning with Gaussian Processes in Multi-Agent Systems: Extended Versioncited by 1
  3. 2024Bayesian meta learning for trustworthy uncertainty quantificationcited by 0
  4. 2025Byzantine-resilient federated online learning for Gaussian process regressioncited by 0