NeurIPS paper proposes functional gradient descent with adaptive representations

A NeurIPS-accepted paper introduces 'adaptive representations,' a framework formalizing approximation schemes for functional gradients that provably converge to the global minimizer and are easy to implement. The resulting algorithms often outperform corresponding neural nets by an order of magnitude across settings.