John Moeller

Machine Learning Researcher

Continuous Kernel Learning

2016-09 ECMLPKDD Riva di Garda, Italy
Author(s): John Moeller, Vivek Srikumar, Sarathkrishna Swaminathan, Suresh Venkatasubramanian, and Dustin Webb

Kernel learning is the problem of determining the best kernel (either from a dictionary of fixed kernels, or from a smooth space of kernel representations) for a given task. In this paper, we describe a new approach to kernel learning that establishes connections between the Fourier-analytic representation of kernels arising out of Bochner's theorem and a specific kind of feed-forward network using cosine activations. We analyze the complexity of this space of hypotheses and demonstrate empirically that our approach provides scalable kernel learning superior in quality to prior approaches.


I defended my PhD thesis in Computer Science at the University of Utah School of Computing, under Suresh Venkatasubramanian. My specialties are in machine learning and algorithms. I am no longer on the market! I'll be joining Kitware in Fall of 2016.