Other regularizations + the Lasso

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Related lectures (29)

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Specification Testing and Machine Learning

Explores specification testing, machine learning, overfitting, regularization, prediction tests, and variable selection.

Introduction to Proximal Operators

Introduces proximal operators and conditional gradient methods for composite convex minimization problems in data optimization.

KKT and Convex Optimization

Covers the KKT conditions and convex optimization, discussing constraint qualifications and tangent cones of convex sets.

Geometry of the Lasso

Explores the geometric explanation of why Lasso solutions are sparse and how coefficients change with the regularization parameter.

Convex Optimization

Introduces convex optimization, focusing on the importance of convexity in algorithms and optimization problems.

Optimal Transport: Rockafellar Theorem

Explores the Rockafellar Theorem in optimal transport, focusing on c-cyclical monotonicity and convex functions.

Regularization in Machine Learning

Explores Ridge and Lasso Regression for regularization in machine learning models, emphasizing hyperparameter tuning and visualization of parameter coefficients.

Proximal and Subgradient Descent: Optimization Techniques

Discusses proximal and subgradient descent methods for optimization in machine learning.

Differentiable Ranking and Sorting

Explores differentiable ranking and sorting techniques for machine learning applications.