Electrochemical impedance spectroscopy (EIS) is a powerful and versatile technique widely used in the study of electrochemical devices and systems . To overcome the limitations of equivalent circuits and physical models when analyzing EIS data , non-parametric methods, such as the distribution of capacitive times , distribution of diffusion times , and distribution of relaxation times (DRT) , have been developed. Despite the more and more widespread usage of the DRT method , extracting meaningful distributions from measured EIS spectra remains challenging due to the ill-posed nature of the inverse problem, leading to non-unique solutions and strong sensitivity to experimental errors . Among the methods used to recover the unknown DRT , regularized regression is the most widespread approach, which formulates DRT inversion as the solution of 𝒙 ̂= argmin