Consistency of Surrogate Risk Minimization Methods for Binary Classification using Strongly Proper Losses

We learnt that under certain conditions on weights, a weighted-average plug-in classifier (or any learning algorithm that outputs such a classifier for the same training sample) is universally Bayes consistent w.r.t 0-1 loss. One might wonder for what other learning algorithms can similar statements be made. Can some of the other commonly studied/used learning algorithms be shown to be Bayes consistent w.r.t. 0-1 loss? We’ve already seen results on Bayes consistency of the ERM algorithm w.r.t 0-1 loss at the expense of computational feasibility. At the other end of the spectrum, we have algorithms like SVM, Logistic Regression etc. that are ubiquitous and computationally feasible but do not directly operate on the 0-1 loss. A natural desideratum in such a situation would be ‘the best of both worlds’ i.e. can we somehow use the minimization of ‘surrogate’ regret by commonly employed learning algorithms as a proxy for minimizing the 0-1 regret?