Implicit bias in optimization algorithms

Abstract: In this talk, we will explore the role of implicit bias in optimization algorithms. Implicit bias refers to the tendency of an algorithm to converge to a specific solution even in the absence of an explicit regularization term in its formulation. In other words, implicit bias emerges from how the algorithm itself interacts with the objective function it’s trying to minimize. We will demonstrate this concept through a few surprising applications.

probabilitystatistics theorydata analysis, statistics and probability

Audience: researchers in the topic

( chat )

Comments: Abstract. In this talk, we will explore the role of implicit bias in optimization algorithms. Implicit bias refers to the tendency of an algorithm to converge to a specific solution even in the absence of an explicit regularization term in its formulation. In other words, implicit bias emerges from how the algorithm itself interacts with the objective function it’s trying to minimize. We will demonstrate this concept through a few surprising applications.

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Asymptotic Methods in Statistics

Series comments: One can find video files and slides of talks at the seminar on Asymptotic Methods in Statistics here: www.youtube.com/@SeminarforAsymptoticMethods

A link to a slide is below the description of the video, namely shorturl.at/jUAhL and shorturl.at/6wjUA shorturl.at/rdpqs

Alas the links do not work in one click: you should emphasize them with a mouse and select, e.g., "Go to shorturl.at/rdpqs".

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