# A Distributionally Robust Boosting Algorithm

## TL;DR

This paper introduces DRO-Boosting, a novel boosting algorithm grounded in Distributionally Robust Optimization, demonstrating its equivalence to AdaBoost and its superior performance on credit default prediction.

## Contribution

It develops a new boosting algorithm based on DRO principles, linking boosting with statistical robustness and showing AdaBoost as a special case.

## Key findings

- DRO-Boosting recovers AdaBoost as a special case.
- The proposed method outperforms existing boosting techniques on credit data.
- DRO framework enhances robustness in boosting algorithms.

## Abstract

Distributionally Robust Optimization (DRO) has been shown to provide a flexible framework for decision making under uncertainty and statistical estimation. For example, recent works in DRO have shown that popular statistical estimators can be interpreted as the solutions of suitable formulated data-driven DRO problems. In turn, this connection is used to optimally select tuning parameters in terms of a principled approach informed by robustness considerations. This paper contributes to this growing literature, connecting DRO and statistics, by showing how boosting algorithms can be studied via DRO. We propose a boosting type algorithm, named DRO-Boosting, as a procedure to solve our DRO formulation. Our DRO-Boosting algorithm recovers Adaptive Boosting (AdaBoost) in particular, thus showing that AdaBoost is effectively solving a DRO problem. We apply our algorithm to a financial dataset on credit card default payment prediction. We find that our approach compares favorably to alternative boosting methods which are widely used in practice.