  Deep kernel learning provides an elegant and principled framework for
combining the structural properties of deep learning algorithms with the
flexibility of kernel methods. By means of a deep neural network, we learn a
parametrized kernel operator that can be combined with a differentiable kernel
algorithm during inference. While previous work within this framework has
focused on learning a single kernel for large datasets, we learn a kernel
family for a variety of few-shot regression tasks. Compared to single deep
kernel learning, our algorithm enables the identification of the appropriate
kernel for each task during inference. As such, it is well adapted for complex
task distributions in a few-shot learning setting, which we demonstrate by
comparing against existing state-of-the-art algorithms using real-world,
few-shot regression tasks related to the field of drug discovery.
