We investigate a bias-corrected machine learning (ML)
strategy for estimating traces of the inverse Dirac operator, $\text{Tr}\,
M^{-n}$ ($n=1,2,3,4$), motivated by the need for higher-order
cumulants of the chiral condensate near the finite-temperature QCD
critical endpoint.
Our supervised regression framework is trained on Wilson-clover
ensembles with the Iwasaki gauge action, and we explore two input
feature scenarios: one using $\text{Tr}\, M^{-1}$ and another relying
solely on gauge observables (plaquette and rectangle), enabling a
fully feature-based prediction pipeline.
Using $\text{Tr}\, M^{-1}$ both as a physical input to cumulant
construction and as a feature for predicting higher powers, we find
that even with $\sim1\%$ labeled data, the resulting susceptibility,
skewness, and kurtosis remain statistically consistent with fully
measured baselines, reducing computational cost to about $26\%$.
In the feature-only approach, where correlations rather than
explicit stochastic traces drive the predictions, bias correction
plays a more pronounced role.
We quantify this impact through multi ensemble reweighting across
nearby quark masses.
Our results demonstrate that bias-corrected ML estimates can significantly reduce measurement overhead while preserving the stability of higher-order observables relevant for locating the QCD critical endpoint.
Code for this work is available at https://github.com/saintbenjamin/Deborah.jl .