In this work, we apply the multi-task SISSO (MT-SISSO) transfer-learning approach to simultaneously model the reactivity towards the three oxidation reactions. Within MT-SISSO, one searches for a single descriptor but allows the fitted coefficients (-. , -0 in eq. 1) adapt to each of the tasks. Here, each reaction corresponds to one task. Thus, the fitted coefficients are different for each reaction i.e., -0 = -0 (7), where 7 corresponds to the reaction. We define the primary features corresponding to activated and spent samples such as they assume different values depending on the modelled reaction, or, equivalently, depending on the task. The application of MT-SISSO with reaction-dependent primary features allows us to obtain universal descriptors for oxidation catalysis, i.e., descriptors that are identical for all the considered reactions, even though the parameters entering in the descriptor expressions and the fitted coefficients assume different values for each reaction.