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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

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

KPC: Learning-Based Model Predictive Control with Deterministic Guarantees

ADAGRAD Avoids Saddle Points

Gradient boosting with extreme-value theory for wildfire prediction

Data-Driven Control and Optimization under Noisy and Uncertain Conditions

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

ADAGRAD Avoids Saddle Points

Federated Learning under Covariate Shifts with Generalization Guarantees

Semantic Perturbations with Normalizing Flows for Improved Generalization