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On double-descent in uncertainty quantification in overparametrized models

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On double-descent in uncertainty quantification in overparametrized models

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Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Expectation consistency for calibration of neural networks

Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial

Stable parameterization of continuous and piecewise-linear functions

Regularization of polynomial networks for image recognition

Bayes-optimal Learning of Deep Random Networks of Extensive-width

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Deep Learning Generalization with Limited and Noisy Labels

Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks

Supervised learning and inference of spiking neural networks with temporal coding

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Deep Learning Generalization with Limited and Noisy Labels

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Regularization of polynomial networks for image recognition

Supervised learning and inference of spiking neural networks with temporal coding

Stable parameterization of continuous and piecewise-linear functions

Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial

Expectation consistency for calibration of neural networks

Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks

Regularization of polynomial networks for image recognition