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Optimal Allocation of Embedding Dimensions under Finite-Sample Constraints

This story is from 2026-08-26. It is preserved in the archive; the latest stories are on the live feed.

arXiv:2608.24592v1 Announce Type: cross Abstract: The embedding dimension of categorical predictors is usually selected through heuristic tuning, although it directly affects model complexity, approximation quality, and finite-sample generalization. This paper formulates embedding dimension selecti…

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  1. 2026-08-26 04:00 · arXiv stat.ML
    Optimal Allocation of Embedding Dimensions under Finite-Sample Constraints

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