Randomly Projected Additive Gaussian Processes for Regression

Understand

Gaussian processes (GPs) provide flexible distributions over functions, with inductive biases controlled by a kernel.

• However, in many applications Gaussian processes can struggle with even moderate input dimensionality.
• Learning a low dimensional projection can help alleviate this curse of dimensionality, but introduces many trainable hyperparameters, which can be cumbersome, especially in the small data regime.
• We use additive sums of kernels for GP regression, where each kernel operates on a different random projection of its inputs.