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High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization

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

A Geometric Unification of Distributionally Robust Covariance Estimators: Shrinking the Spectrum by Inflating the Ambiguity Set

Saddle-to-Saddle Dynamics in Diagonal Linear Networks

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

(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large Stepsizes and Edge of Stability

A Neural-Network-Based Convex Regularizer for Inverse Problems

Acute TNF alpha levels predict cognitive impairment 6-9 months after COVID-19 infection

Regularization Techniques for Low-Resource Machine Translation

Modeling tumor size dynamics based on real-world electronic health records and image data in advanced melanoma patients receiving immunotherapy