Distributionally Robust Multi-Output Regression Ranking

TL;DR

This paper introduces DRMRR, a robust listwise learning-to-rank model that effectively handles data errors, shifts, and adversarial perturbations, outperforming existing models in real-world applications.

Contribution

The paper proposes a novel distributionally robust multi-output ranking model using Wasserstein-based DRO, capturing local and cross-document interactions for improved robustness.

Findings

DRMRR outperforms state-of-the-art LTR models in medical retrieval and drug response tasks.

DRMRR maintains stable performance under various noise conditions.

The model effectively resists adversarial and label noise perturbations.

Abstract

Despite their empirical success, most existing listwiselearning-to-rank (LTR) models are not built to be robust to errors in labeling or annotation, distributional data shift, or adversarial data perturbations. To fill this gap, we introduce a new listwise LTR model called Distributionally Robust Multi-output Regression Ranking (DRMRR). Different from existing methods, the scoring function of DRMRR was designed as a multivariate mapping from a feature vector to a vector of deviation scores, which captures local context information and cross-document interactions. DRMRR uses a Distributionally Robust Optimization (DRO) framework to minimize a multi-output loss function under the most adverse distributions in the neighborhood of the empirical data distribution defined by a Wasserstein ball. We show that this is equivalent to a regularized regression problem with a matrix norm regularizer. Our experiments were conducted on two real-world applications, medical document retrieval, and drug response prediction, showing that DRMRR notably outperforms state-of-the-art LTR models. We also conducted a comprehensive analysis to assess the resilience of DRMRR against various types of noise: Gaussian noise, adversarial perturbations, and label poisoning. We show that DRMRR is not only able to achieve significantly better performance than other baselines, but it can maintain a relatively stable performance as more noise is added to the data.