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stat.ML2024
Minimax optimality of deep neural networks on dependent data via PAC-Bayes bounds
Pierre Alquier, William Kengne
In a groundbreaking work, Schmidt-Hieber (2020) proved the minimax optimality of deep neural networks with ReLu activation for least-square regression estimation over a large class…
stat.ML2024
Deep learning from strongly mixing observations: Sparse-penalized regularization and minimax optimality
William Kengne, Modou Wade
The explicit regularization and optimality of deep neural networks estimators from independent data have made considerable progress recently. The study of such properties on depend…
stat.ML2024
Robust deep learning from weakly dependent data
William Kengne, Modou Wade
Recent developments on deep learning established some theoretical properties of deep neural networks estimators. However, most of the existing works on this topic are restricted to…