Establishing Linear Surrogate Regret Bounds for Convex Smooth Losses via Convolutional Fenchel–Young Losses
Yuzhou Cao, Han Bao, Lei Feng, Bo An
Surrogate regret bounds, also known as excess risk bounds, bridge the gap between the convergence rates of surrogate and target losses, with linear bounds favorable for their lossless regret transfer. While convex smooth surrogate losses are appealing in particular due to the efficient estimation and optimization, the existence of a trade-off between the smoothness and linear regret bound has been believed in the community. That being said, the better optimization and estimation properties of convex smooth surrogate losses may inevitably deteriorate after undergoing the regret transfer onto a target loss. Strikingly, we overcome this dilemma for arbitrary discrete target losses by constructing a convex smooth surrogate loss, which entails a linear surrogate regret bound composed with a tailored prediction link. The construction is based on Fenchel--Young losses generated by the $\textit{convolutional negentropy}$,which is relevant to the infimal convolution of a generalized negentropy and the target Bayes risk. Consequently, the infimal convolution enables us to derive a smooth loss while maintaining the regret bound linear. We additionally benefit from the infimal convolution to have a consistent estimator of the linear property, which serves as prediction confidence. Our results are overall a novel demonstration of how convex analysis penetrates into optimization and statistical efficiency in risk minimization.