# Transductive Log Opinion Pool of Gaussian Process Experts

arXiv: https://arxiv.org/abs/1511.07551 · HTML: https://mlsanity.com/paper/1511.07551/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2015.

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

- [Hierarchical Policy Blending as Inference for Reactive Robot Control](https://mlsanity.com/paper/2210.07890/) (2022)
- [Healing Products of Gaussian Processes](https://mlsanity.com/paper/2102.07106/) (2021)
- [Bayesian Optimisation Using Product-of-Experts Gaussian Process Models with Uncertainty Calibration](https://mlsanity.com/paper/2609.15555/) (2026)

## Abstract (arXiv)

We introduce a framework for analyzing transductive combination of Gaussian process (GP) experts, where independently trained GP experts are combined in a way that depends on test point location, in order to scale GPs to big data. The framework provides some theoretical justification for the generalized product of GP experts (gPoE-GP) which was previously shown to work well in practice but lacks theoretical basis. Based on the proposed framework, an improvement over gPoE-GP is introduced and empirically validated.