Transductive Log Opinion Pool of Gaussian Process Experts
This work addresses the scalability issue in Gaussian processes for machine learning practitioners, but it is incremental as it builds on existing methods.
The authors tackled the problem of scaling Gaussian processes to big data by proposing a framework for transductive combination of GP experts, which provides theoretical justification for the generalized product of GP experts (gPoE-GP) and introduces an improved version that is empirically validated.
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.