SUPR
Complexity in Machine Learning
Dnr:

NAISS 2025/22-1033

Type:

NAISS Small Compute

Principal Investigator:

Daniel Buncic

Affiliation:

Stockholms universitet

Start Date:

2025-07-31

End Date:

2026-08-01

Primary Classification:

50202: Business Administration

Webpage:

Allocation

Abstract

The goal of the project is to examine the sensitive of various machine learning algorithms such as random Fourier features (RFFs) to different assumptions about various data standardization, the inclusion of intercept terms, as well as aggregation methods.