Understanding Generalization: Implicit Bias & Optimization

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Page 3 of 3

Covers stochastic gradient descent, linear regression, regularization, supervised learning, and the iterative nature of gradient descent.

Covers optimization algorithms, stable matching, and Big-O notation for algorithm efficiency.

Explores the optimality of convergence rates in convex optimization, focusing on accelerated gradient descent and adaptive methods.

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Covers trust region methods, focusing on the framework and algorithms.

Covers polynomial regression, gradient descent, overfitting, underfitting, regularization, and feature scaling in optimization algorithms.

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

Explores adaptive gradient methods like AdaGrad, AcceleGrad, and UniXGrad, focusing on their local adaptation and convergence rates.