# Target contrastive pessimistic risk for robust domain adaptation

## TL;DR

This paper introduces a robust domain adaptation method that ensures classifiers do not perform worse than non-adaptive ones by using a conservative risk estimation approach, improving performance across various domain shifts.

## Contribution

It proposes a novel target contrastive pessimistic risk framework that guarantees non-degradation in performance and enhances robustness in domain adaptation tasks.

## Key findings

- Performs on par with state-of-the-art in sample selection bias settings.
- Outperforms existing methods in more general domain adaptation scenarios.
- Ensures non-worse performance compared to non-adaptive classifiers.

## Abstract

In domain adaptation, classifiers with information from a source domain adapt to generalize to a target domain. However, an adaptive classifier can perform worse than a non-adaptive classifier due to invalid assumptions, increased sensitivity to estimation errors or model misspecification. Our goal is to develop a domain-adaptive classifier that is robust in the sense that it does not rely on restrictive assumptions on how the source and target domains relate to each other and that it does not perform worse than the non-adaptive classifier. We formulate a conservative parameter estimator that only deviates from the source classifier when a lower risk is guaranteed for all possible labellings of the given target samples. We derive the classical least-squares and discriminant analysis cases and show that these perform on par with state-of-the-art domain adaptive classifiers in sample selection bias settings, while outperforming them in more general domain adaptation settings.