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arXiv Machine Learning By Chaitanya Dumbre, Sourabh Dube

Estimating Spectrum-Free Information Bounds for Generative Surrogates Beyond Conditional Collapse

Read the original on arXiv Machine Learning →

The paper evaluates conditional generative surrogates used in high‑energy physics, noting that common 1D marginal divergence metrics can mistakenly reward models that ignore conditioning information. Using the Continuous Ranked Probability Skill Score (CRPSS) as a more robust metric, the authors demonstrate that a simple sampler can outperform a trained Mixture Density Network on all target marginals, and that the advantage diminishes when CRPSS is evaluated within bins of the conditioning variable. They further introduce a data‑driven k‑Nearest‑Neighbor extrapolation to estimate the maximum achievable CRPSS given the conditioning information, finding that both models reach this estimate, suggesting current architectures have extracted the available conditioning information for the evaluated targets.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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