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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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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

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

Stable parameterization of continuous and piecewise-linear functions

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

Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks

Expectation consistency for calibration of neural networks

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

Regularization of polynomial networks for image recognition

Regularization of polynomial networks for image recognition

Deep Learning Generalization with Limited and Noisy Labels

Supervised learning and inference of spiking neural networks with temporal coding