Computational trade-offs in synthetic data generation and use

Determine optimal strategies for balancing synthetic data quality and quantity against computational cost to maximize downstream statistical utility, including guidance on how many synthetic samples to generate and how to weight them relative to real data.

Background

Training high‑fidelity generative models and producing large volumes of synthetic data can be computationally expensive. Downstream use introduces additional design choices (e.g., how many synthetic samples to generate, how to weight them).

The paper identifies the need for principles that navigate the statistical–computational trade‑off, aiming to allocate computational resources where they yield the most statistical benefit.