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

Understand

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.