Distribution-Based Invariant Deep Networks for Learning Meta-Features
Recent advances in deep learning from probability distributions enable to achieve classification or regression from distribution samples, invariant under permutation of the samples. This paper extends the distribution-based deep neural architectures to achieve classification or regression from distribution samples, invariant under permutation of the descriptive features, too. The motivation for this extension is the Auto-ML problem, aimed to identify a priori the ML configuration best suited to a dataset. Formally, a distribution-based invariant deep learning architecture is presented, and leveraged to extract the meta-features characterizing a dataset. The contribution of the paper is twofold. On the theoretical side, the proposed architecture inherits the NN properties of universal approximation, and the robustness of the approach w.r.t. moderate perturbations is established. On the empirical side, a proof of concept of the approach is proposed, to identify the SVM hyper-parameters best suited to a large benchmark of diversified small size datasets.