mlsanityData to 7 Oct 2026

Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions

arXiv 1410.7827 ↗First posted 28 Oct 2014cs.LG cs.AI stat.MLcited by 66

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Abstract (arXiv)

In this work, we propose a generalized product of experts (gPoE) framework for combining the predictions of multiple probabilistic models. We identify four desirable properties that are important for scalability, expressiveness and robustness, when learning and inferring with a combination of multiple models. Through analysis and experiments, we show that gPoE of Gaussian processes (GP) have these qualities, while no other existing combination schemes satisfy all of them at the same time. The resulting GP-gPoE is highly scalable as individual GP experts can be independently learned in parallel; very expressive as the way experts are combined depends on the input rather than fixed; the combined prediction is still a valid probabilistic model with natural interpretation; and finally robust to unreliable predictions from individual experts.

Key facts. Cited by 66 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Oct 2014.

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Citing papers per year: 2020: 1; 2021: 13; 2022: 18; 2023: 10; 2024: 12; 2025: 4; 2026: 8.

66 papers.

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