To Kernels and Back Again: A Study of Empirical Phenomena in Machine Learning

This talk presents work that uses classical kernel methods to study empirical phenomena in ML, including structured kernel constructions for scientific tasks and an investigation of whether the high predictive performance of neural networks on standard benchmarks is fundamental or due to overfitting on test sets.