Distribution-Based Invariant Deep Networks for Learning Meta-Features

Gwendoline de Bie, Herilalaina Rakotoarison, Gabriel Peyré, Michèle Sebag

Keywords: invariant neural networks, universal approximation, meta-feature learning

Abstract: Recent advances in deep learning from probability distributions successfully achieve classification or regression from distribution samples, thus invariant under permutation of the samples. The first contribution of the paper is to extend these neural architectures to achieve invariance under permutation of the features, too. The proposed architecture, called Dida, inherits the NN properties of universal approximation, and its robustness with respect to Lipschitz-bounded transformations of the input distribution is established. The second contribution is to empirically and comparatively demonstrate the merits of the approach on two tasks defined at the dataset level. On both tasks, Dida learns meta-features supporting the characterization of a (labelled) dataset. The first task consists of predicting whether two dataset patches are extracted from the same initial dataset. The second task consists of predicting whether the learning performance achieved by a hyper-parameter configuration under a fixed algorithm (ranging in k-NN, SVM, logistic regression and linear SGD) dominates that of another configuration, for a dataset extracted from the OpenML benchmarking suite. On both tasks, Dida outperforms the state of the art: DSS and Dataset2Vec architectures, as well as the models based on the hand-crafted meta-features of the literature.

One-sentence Summary: Existing distributional-based neural network are extended to achieve invariance under permutation of the features, with theoritical guarantees of universal approximation and robustness, suitable for learning dataset meta-features.

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Reviewed Version (pdf): https://openreview.net/references/pdf?id=ryBTdoj_dp