A nonasymptotic law of iterated logarithm for general M-estimators

TL;DR

This paper introduces the first non-asymptotic deviation bounds for general M-estimators, providing robust, anytime guarantees applicable to machine learning tasks like bandit problems.

Contribution

It develops non-asymptotic, anytime deviation bounds for M-estimators under general conditions, enabling new robust algorithms with optimal guarantees.

Findings

Bounds hold with high probability for all sample sizes.

Applicable to heavy-tailed and outlier-prone data.

Numerical experiments confirm theoretical results.

Abstract

M-estimators are ubiquitous in machine learning and statistical learning theory. They are used both for defining prediction strategies and for evaluating their precision. In this paper, we propose the first non-asymptotic "any-time" deviation bounds for general M-estimators, where "any-time" means that the bound holds with a prescribed probability for every sample size. These bounds are nonasymptotic versions of the law of iterated logarithm. They are established under general assumptions such as Lipschitz continuity of the loss function and (local) curvature of the population risk. These conditions are satisfied for most examples used in machine learning, including those ensuring robustness to outliers and to heavy tailed distributions. As an example of application, we consider the problem of best arm identification in a parametric stochastic multi-arm bandit setting. We show that the established bound can be converted into a new algorithm, with provably optimal theoretical guarantees. Numerical experiments illustrating the validity of the algorithm are reported.