On Lipschitz Continuity and Smoothness of Loss Functions in Learning to Rank

TL;DR

This paper explores how Lipschitz continuity and smoothness of loss functions influence generalization bounds in learning to rank, proposing improved bounds and rates that enhance theoretical understanding and practical guarantees.

Contribution

It introduces the importance of norm choice in defining Lipschitz and smoothness properties for vector predictions, leading to improved bounds and rates in learning to rank.

Findings

Improved generalization bounds using $\, ext{ extltilde}\,$-norm

Rates interpolating between $1/ ext{sqrt}(n)$ and $1/n$ under smoothness

State-of-the-art guarantees for ListNet

Abstract

In binary classification and regression problems, it is well understood that Lipschitz continuity and smoothness of the loss function play key roles in governing generalization error bounds for empirical risk minimization algorithms. In this paper, we show how these two properties affect generalization error bounds in the learning to rank problem. The learning to rank problem involves vector valued predictions and therefore the choice of the norm with respect to which Lipschitz continuity and smoothness are defined becomes crucial. Choosing the $\ell_\infty$ norm in our definition of Lipschitz continuity allows us to improve existing bounds. Furthermore, under smoothness assumptions, our choice enables us to prove rates that interpolate between $1/\sqrt{n}$ and $1/n$ rates. Application of our results to ListNet, a popular learning to rank method, gives state-of-the-art performance guarantees.