A Simple Illustration of Interleaved Learning using Kalman Filter for Linear Least Squares

TL;DR

This paper demonstrates how interleaved learning can be understood through a Kalman Filter framework applied to linear least squares problems, providing a simple illustrative example.

Contribution

It introduces a straightforward statistical and optimization framework using Kalman Filter to illustrate interleaved learning mechanisms.

Findings

Kalman Filter can model interleaved learning in linear least squares.

The framework offers a clear illustration of interleaving in machine learning.

Potential for applying this approach to biologically inspired learning algorithms.

Abstract

Interleaved learning in machine learning algorithms is a biologically inspired training method with promising results. In this short note, we illustrate the interleaving mechanism via a simple statistical and optimization framework based on Kalman Filter for Linear Least Squares.