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Explores learning the kernel solution in convex optimization, focusing on predicting outputs using a linear classifier and addressing possible numerical issues.
Covers the role of models and data in statistical learning and optimization formulations, with examples of classification, regression, and density estimation problems.
Explores the impact of gradient noise on optimization algorithms, focusing on smooth and nonsmooth risk functions and the derivation of gradient noise moments.
