Submodular maximization under a Gaussian model selects small benchmark subsets that outperform random selection for imputing leaderboard scores, with mutual information better than entropy at small sizes.
E Model Canonicalization To merge score matrices from different benchmark collections into a unified table, we must identify when the same model appears under different names
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Submodular Benchmark Selection
Submodular maximization under a Gaussian model selects small benchmark subsets that outperform random selection for imputing leaderboard scores, with mutual information better than entropy at small sizes.