# Whom to Trust? Elective Learning for Distributed Gaussian Process Regression

arXiv: https://arxiv.org/abs/2402.03014 · HTML: https://mlsanity.com/paper/2402.03014/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 5 Feb 2024.

## Builds on (most cited first, 10 of 10)

- [On Kernelized Multi-armed Bandits](https://mlsanity.com/paper/1704.00445/) (2017)
- [Distributed Gaussian Processes](https://mlsanity.com/paper/1502.02843/) (2015)
- [Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions](https://mlsanity.com/paper/1410.7827/) (2014)
- [Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control](https://mlsanity.com/paper/1906.01376/) (2019)
- [Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression](https://mlsanity.com/paper/1806.00720/) (2018)
- [Deterministic error bounds for kernel-based learning techniques under bounded noise](https://mlsanity.com/paper/2008.04005/) (2020)
- [Recursive Estimation for Sparse Gaussian Process Regression](https://mlsanity.com/paper/1905.11711/) (2019)
- [Distributed Learning Consensus Control for Unknown Nonlinear Multi-Agent Systems based on Gaussian Processes](https://mlsanity.com/paper/2103.15929/) (2021)
- [Model Reference Gaussian Process Regression: Data-Driven State Feedback Controller](https://mlsanity.com/paper/2303.09828/) (2023)
- [Learning-based Control for PMSM Using Distributed Gaussian Processes with Optimal Aggregation Strategy](https://mlsanity.com/paper/2307.13945/) (2023)

## Built on by (most cited first, 3 of 3)

- [A Nested Model for AI Design and Validation](https://mlsanity.com/paper/2407.16888/) (2024)
- [AI Readiness in Healthcare through Storytelling XAI](https://mlsanity.com/paper/2410.18725/) (2024)
- [Quality or Quantity? Error-Informed Selective Online Learning with Gaussian Processes in Multi-Agent Systems: Extended Version](https://mlsanity.com/paper/2601.14275/) (2026)

## Abstract (arXiv)

This paper introduces an innovative approach to enhance distributed cooperative learning using Gaussian process (GP) regression in multi-agent systems (MASs). The key contribution of this work is the development of an elective learning algorithm, namely prior-aware elective distributed GP (Pri-GP), which empowers agents with the capability to selectively request predictions from neighboring agents based on their trustworthiness. The proposed Pri-GP effectively improves individual prediction accuracy, especially in cases where the prior knowledge of an agent is incorrect. Moreover, it eliminates the need for computationally intensive variance calculations for determining aggregation weights in distributed GP. Furthermore, we establish a prediction error bound within the Pri-GP framework, ensuring the reliability of predictions, which is regarded as a crucial property in safety-critical MAS applications.