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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

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Topics in statistical physics of high-dimensional machine learning

Machine Learning for Modeling Stock Returns

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

Benign Overfitting in Deep Neural Networks under Lazy Training

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks

Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design

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