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Neural Tanget Kernel: Convergence and Generalization in Neural Networks

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Generalization of Scaled Deep ResNets in the Mean-Field Regime

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Topics in statistical physics of high-dimensional machine learning

Machine Learning for Modeling Stock Returns

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

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

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

Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks

Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design

Benign Overfitting in Deep Neural Networks under Lazy Training

Deep Learning Theory Through the Lens of Diagonal Linear Networks

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

Machine Learning for Modeling Stock Returns

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Topics in statistical physics of high-dimensional machine learning

Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks

Benign Overfitting in Deep Neural Networks under Lazy Training

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

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

Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design