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Early stopping

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

Gradient boosting with extreme-value theory for wildfire prediction

Data-Driven Control and Optimization under Noisy and Uncertain Conditions

Federated Learning under Covariate Shifts with Generalization Guarantees

Classification of fall directions via wearable motion sensors

The very knotty lenser: Exploring the role of regularization in source and potential reconstructions using Gaussian process regression

ADAGRAD Avoids Saddle Points

ADAGRAD Avoids Saddle Points

Semantic Perturbations with Normalizing Flows for Improved Generalization

KPC: Learning-Based Model Predictive Control with Deterministic Guarantees

Federated Learning under Covariate Shifts with Generalization Guarantees

Data-Driven Control and Optimization under Noisy and Uncertain Conditions

Gradient boosting with extreme-value theory for wildfire prediction

The very knotty lenser: Exploring the role of regularization in source and potential reconstructions using Gaussian process regression

KPC: Learning-Based Model Predictive Control with Deterministic Guarantees

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

Classification of fall directions via wearable motion sensors

ADAGRAD Avoids Saddle Points

Semantic Perturbations with Normalizing Flows for Improved Generalization

ADAGRAD Avoids Saddle Points