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Using Invariant Predictions to Harness Spurious Features
arXiv
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arXiv
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由 C Eastwood 著作2023被引用 12 次 — Abstract page for arXiv paper 2307.09933: Spuriosity Didn't Kill the Classifier: Using Invariant Predictions to Harness Spurious Features.
Using Invariant Predictions to Harness Spurious Features
OpenReview
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The paper shows that spurious features can be harnessed in the test domain without labels, using only invariant-feature pseudo-labels, and propose an algorithm ...
Using Invariant Predictions to Harness Spurious Features
NIPS papers
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NIPS papers
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由 C Eastwood 著作2024被引用 12 次 — We prove that predictions based on XS can be used to safely harness a sub-component of XU (dark-orange region).
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Using Invariant Predictions to Harness Spurious Features
NeurIPS 2024
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NeurIPS 2024
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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 ...
Using Invariant Predictions to Harness Spurious Features
Andrei Liviu Nicolicioiu
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Andrei Liviu Nicolicioiu
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由 B Schölkopf 著作 — We show when and how it is possible to safely harness spurious or unstable features without test-domain labels. We prove that predictions based on invariant ...
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Using Invariant Predictions to Harness Spurious Features
OpenReview
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由 C Eastwood 著作被引用 12 次 — Spuriosity Didn't Kill the Classifier: Using Invariant Predictions to Harness Spurious Features. Cian Eastwood. *12. Shashank Singh. * 1. Andrei L. Nicolicioiu.
Spuriosity didn't kill the classifier - ACM Digital Library
ACM Digital Library
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2024年5月30日 — We propose Stable Feature Boosting (SFB), an algorithm for: (i) learning a predictor that separates stable and conditionally-independent unstable features;
Spuriosity Didn't Kill the Classifier: Using Invariant Predictions ...
Max Planck Institute for Intelligent Systems
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Max Planck Institute for Intelligent Systems
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Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and ...
Using Invariant Predictions to Harness Spurious Features
NIPS papers
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NIPS papers
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Spuriosity Didn't Kill the Classifier: Using Invariant. Predictions to ... • Algorithmic: We propose the Stable Feature Boosting (SFB) algorithm for using stable/ ...
Using Invariant Predictions to Harness Spurious Features
ResearchGate
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ResearchGate
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To avoid failures on out-of-distribution data, recent works have sought to extract features that have a stable or invariant relationship with the label ...