Spuriosity Didn't Kill the Classifier: Using Invariant Predictions to Harness Spurious Features

Autor:	Eastwood, Cian, Singh, Shashank, Nicolicioiu, Andrei Liviu, Vlastelica, Marin, von Kügelgen, Julius, Schölkopf, Bernhard
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Computer Vision and Pattern Recognition Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	To avoid failures on out-of-distribution data, recent works have sought to extract features that have an invariant or stable relationship with the label across domains, discarding "spurious" or unstable features whose relationship with the label changes across domains. However, unstable features often carry complementary information that could boost performance if used correctly in the test domain. In this work, we show how this can be done without test-domain labels. In particular, we prove that pseudo-labels based on stable features provide sufficient guidance for doing so, provided that stable and unstable features are conditionally independent given the label. Based on this theoretical insight, we propose Stable Feature Boosting (SFB), an algorithm for: (i) learning a predictor that separates stable and conditionally-independent unstable features; and (ii) using the stable-feature predictions to adapt the unstable-feature predictions in the test domain. Theoretically, we prove that SFB can learn an asymptotically-optimal predictor without test-domain labels. Empirically, we demonstrate the effectiveness of SFB on real and synthetic data. Comment: NeurIPS 2023 Camera-Ready
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2307.09933 Zobrazit plný text záznamu View this record from Arxiv