GAME is a convex estimator using overlapping nuclear-norm penalties on subgroup submatrices for low-rank matrix completion with known overlapping groups, providing finite-sample guarantees on reconstruction error and subgroup subspace recovery.
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DKPS-based methods predict new model benchmark scores using cached responses, matching baseline mean absolute error with substantially fewer queries and an offline query selection approach.
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Group-Aware Matrix Estimation and Latent Subspace Recovery
GAME is a convex estimator using overlapping nuclear-norm penalties on subgroup submatrices for low-rank matrix completion with known overlapping groups, providing finite-sample guarantees on reconstruction error and subgroup subspace recovery.
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Query-efficient model evaluation using cached responses
DKPS-based methods predict new model benchmark scores using cached responses, matching baseline mean absolute error with substantially fewer queries and an offline query selection approach.