  In this study, we address the problem of high-dimensional binary
classification. Our proposed solution involves employing an aggregation
technique founded on exponential weights and empirical hinge loss. Through the
employment of a suitable sparsity-inducing prior distribution, we demonstrate
that our method yields favorable theoretical results on prediction error. The
efficiency of our procedure is achieved through the utilization of Langevin
Monte Carlo, a gradient-based sampling approach. To illustrate the
effectiveness of our approach, we conduct comparisons with the logistic Lasso
on simulated data and a real dataset. Our method frequently demonstrates
superior performance compared to the logistic Lasso.
