Search Results for author:
Found 3 papers, 1 papers with code
Gradient-based bilevel optimization for multi-penalty Ridge regression through matrix differential calculus
Common regularization algorithms for linear regression, such as LASSO and Ridge regression, rely on a regularization hyperparameter that balances the tradeoff between minimizing the fitting error and the norm of the learned model coefficients.
Split-Boost Neural Networks
The calibration and training of a neural network is a complex and time-consuming procedure that requires significant computational resources to achieve satisfactory results.
Hedging using reinforcement learning: Contextual $k$-Armed Bandit versus $Q$-learning
In this article, the hedging problem is viewed as an instance of a risk-averse contextual $k$-armed bandit problem, which is motivated by the simplicity and sample-efficiency of the architecture.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
