Lectures related to Implicit Bias in Machine Learning

Variance Reduction in Deep Learning

Explores variance reduction techniques in deep learning, covering gradient descent, stochastic gradient descent, SVRG method, and performance comparison of algorithms.

The Hidden Convex Optimization Landscape of Deep Neural Networks

Explores the hidden convex optimization landscape of deep neural networks, showcasing the transition from non-convex to convex models.

Faster Gradient Descent: Projected Optimization Techniques

Covers faster gradient descent methods and projected gradient descent for constrained optimization in machine learning.

Gradient Descent

Covers the concept of gradient descent, a universal algorithm used to find the minimum of a function.

Optimality of Convergence Rate: Acceleration in Gradient Descent

Explores the optimality of convergence rate in gradient descent and acceleration techniques for convex and non-convex problems.

Optimal Control: KKT Conditions

Explores optimal control and KKT conditions for non-linear optimization with constraints.

Gradient Descent: Principles and Applications

Covers gradient descent, its principles, applications, and convergence rates in optimization for machine learning.