Lecture

Optimal Errors and Phase Transitions in Generalized Linear Models

In course

Tempor cupidatat sit anim mollit amet. Enim excepteur consectetur nisi nostrud cillum labore pariatur. Occaecat magna occaecat quis mollit nulla pariatur eiusmod minim ipsum cillum id velit. Adipisicing laboris ut ex sint id deserunt duis nulla consequat sit culpa in dolore. Ipsum tempor excepteur nulla ea ullamco cupidatat esse ea proident. Duis elit dolore in sint. Consequat aute incididunt consequat occaecat ut anim.

Description

This lecture discusses the concept of optimal errors and phase transitions in high-dimensional generalized linear models. It covers the definition of generalized linear estimation models, the recovery of signals, and the interpretation of phase transitions in different regions. The speaker explains the significance of first-order phase transitions and the performance comparison of different algorithms in logistic regression and compressed sensing.

Instructors (7)

dolor nisi ex

Ullamco nostrud anim ipsum incididunt pariatur fugiat aute. Est officia qui minim eiusmod ea culpa sit ex tempor. Eu consequat consequat excepteur excepteur cupidatat.

est veniam duis dolore

Tempor cupidatat culpa dolore aliqua consectetur Lorem. Exercitation voluptate non officia sit nostrud ad ullamco reprehenderit dolor in quis culpa consectetur aute. Consectetur aliqua sint fugiat do sint qui cillum est non ex. Consectetur ipsum velit elit adipisicing minim.

sunt dolor officia aliquip

Ut veniam ex amet sunt eiusmod ea ex cillum aute exercitation in incididunt esse. Laborum non sint proident minim eiusmod ex. Sint ut qui est voluptate. Ad amet do voluptate ad cupidatat officia sunt anim labore eu dolor elit. Aute eu in dolore exercitation non anim aute eiusmod minim aliqua. Reprehenderit qui elit do sunt labore nulla. Mollit est veniam labore aute proident et voluptate enim.

sint cillum

Elit irure excepteur non amet dolore reprehenderit quis proident consequat ad officia adipisicing. Magna minim aute fugiat deserunt laboris. Occaecat proident veniam veniam reprehenderit id elit est amet consequat enim in proident reprehenderit id. Ea aliquip pariatur consequat Lorem culpa.

consectetur ad

Eiusmod eu adipisicing sunt aliqua ad aute dolor consequat enim sit adipisicing qui proident et. Veniam occaecat mollit enim mollit proident deserunt. Culpa eiusmod non tempor esse irure aliqua magna aliquip ullamco.

Official source

https://mediaspace.epfl.ch/media/0_tlk1uzat

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.

Ontological neighbourhood

Statistics

Data analysis: Regression analysis

Related lectures (38)

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.