Memory-Efficient Adaptive Optimization

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Stochastic Gradient Descent: Non-convex Optimization Techniques

Discusses Stochastic Gradient Descent and its application in non-convex optimization, focusing on convergence rates and challenges in machine learning.

Structures in Non-Convex Optimization

Explores non-convex optimization in deep learning, covering critical points, SGD convergence, saddle points, and adaptive gradient methods.

Stochastic Optimization: Algorithms and Methods

Explores stochastic optimization algorithms and methods for convex problems with smooth and nonsmooth risks.

Non-Convex Optimization: Techniques and Applications

Covers non-convex optimization techniques and their applications in machine learning.

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.

Optimization Techniques: Stochastic Gradient Descent and Beyond

Discusses optimization techniques in machine learning, focusing on stochastic gradient descent and its applications in constrained and non-convex problems.

Proximal Gradient Descent: Optimization Techniques in Machine Learning

Discusses proximal gradient descent and its applications in optimizing machine learning algorithms.

Policy Gradient Methods: Convergence and Optimization

Covers the convergence of policy gradient methods and their optimization in reinforcement learning.

Convex Optimization

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

Convex Optimization: Gradient Algorithms

Covers convex optimization problems and gradient-based algorithms to find the global minimum.