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This post discusses the use of linear regression with regularization and the challenges of setting the regularization parameters. It introduces the concept of data-driven algorithm design and provides sample complexity guarantees for learning the optimal parameters. The post also explores the extension to binary classification and kernel regression. The research opens up possibilities for tuning learnable parameters in continuous optimization problems.

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