An anytime algorithm for learning loss functions that is asymptotically optimal in the worst case and experimentally faster than prior methods for hyperparameter tuning.
Regularization and variable selection via the elastic net
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Learning Effective Loss Functions Efficiently
An anytime algorithm for learning loss functions that is asymptotically optimal in the worst case and experimentally faster than prior methods for hyperparameter tuning.