March 13, 2024, 4:43 a.m. | Shirong Xu, Will Wei Sun, Guang Cheng

cs.LG updates on

arXiv:2305.10015v2 Announce Type: replace-cross
Abstract: Synthetic data algorithms are widely employed in industries to generate artificial data for downstream learning tasks. While existing research primarily focuses on empirically evaluating utility of synthetic data, its theoretical understanding is largely lacking. This paper bridges the practice-theory gap by establishing relevant utility theory in a statistical learning framework. It considers two utility metrics: generalization and ranking of models trained on synthetic data. The former is defined as the generalization difference between models trained …

abstract algorithms artificial arxiv cs.lg data gap generate industries paper practice research statistical synthetic synthetic data tasks theory type understanding utility

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