Lecture

Convergence Analysis: Stochastic Gradient Algorithms

In course
DEMO: cillum deserunt sunt
Ipsum et consequat aliquip nulla laborum non veniam nulla ipsum. Aliqua adipisicing adipisicing quis nulla veniam do consectetur officia et quis incididunt id. Eu proident veniam pariatur elit labore excepteur occaecat amet aliquip Lorem dolor nulla. In officia sint tempor et esse aute laborum velit. Commodo sint ad voluptate dolore aliquip laboris pariatur magna commodo ex adipisicing. Amet nisi dolore aliqua Lorem sit Lorem.
Login to see this section
Description

This lecture covers the convergence analysis of stochastic gradient algorithms for smooth risks under various operational modes, including updates with constant and vanishing step-sizes, data sampling with and without replacement, and mini-batch gradient approximations. The lecture delves into the conditions on risk and loss functions, the convergence behavior in mean-square-error sense, and the impact of step-size sequences on the convergence rate. The instructor discusses the convergence properties under different step-size sequences and provides theorems and examples to illustrate the rates of convergence.

Instructor
irure exercitation
Anim consequat reprehenderit dolore laboris et deserunt eu do. Nisi Lorem esse velit tempor velit consectetur ad duis reprehenderit sint. Laboris enim aliquip esse ipsum cillum aliquip qui. Adipisicing reprehenderit aliqua reprehenderit nisi. Commodo culpa anim incididunt in pariatur sit id fugiat proident incididunt.
Login to see this section
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Related lectures (32)
Stochastic Optimization: Algorithms and Methods
Explores stochastic optimization algorithms and methods for convex problems with smooth and nonsmooth risks.
Adaptive Gradient Methods: Part 1
Explores adaptive gradient methods and their impact on optimization scenarios, including AdaGrad, ADAM, and RMSprop.
Recursive Least-Squares: Weighted Formulation
Covers the Recursive Least-Squares algorithm with weighted formulation for real-time data updating.
Optimization: Gradient Descent and Subgradients
Explores optimization methods like gradient descent and subgradients for training machine learning models, including advanced techniques like Adam optimization.
Boltzmann Machine
Introduces the Boltzmann Machine, covering expectation consistency, data clustering, and probability distribution functions.
Show more

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.