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OOD-CHAMELEON: Is Algorithm Selection for OOD Generalization Learnable? episode artwork

EPISODE · Oct 4, 2024 · 21 MIN

OOD-CHAMELEON: Is Algorithm Selection for OOD Generalization Learnable?

from Arxiv Papers · host Igor Melnyk

The paper introduces OOD-CHAMELEON, a method for selecting algorithms for out-of-distribution generalization by predicting performance based on dataset characteristics, outperforming individual algorithms and heuristics. https://arxiv.org/abs//2410.02735 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

Episode metadata supplied by the publisher feed · Published Oct 4, 2024

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OOD-CHAMELEON: Is Algorithm Selection for OOD Generalization Learnable?

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