  We use a novel method to constrain the neutrino magnetic dipole moment
($\mu_{\nu}$) using the empirically-calibrated tip of the red giant branch
I-band magnitude that fully accounts for uncertainties in stellar physics. Our
method uses machine learning to emulate the results of stellar evolution codes.
This reduces the I-Band magnitude computation time to milliseconds, which
enables a Bayesian statistical analysis where $\mu_{\nu}$ is varied
simultaneously with the stellar physics, allowing for a complete exploration of
parameter space. We find the region $\mu_{\nu} \leq
6\times10^{-12}\mu_{\textrm{B}}$ (with $\mu_{\textrm{B}}$ the Bohr magneton),
previously believed to be excluded, is unconstrained after accounting for
degeneracies with stellar physics. It is likely that larger values are
similarly unconstrained. We discuss the implications of our results for future
neutrino magnetic dipole moment searches and for other astrophysical probes.
