Feb. 9, 2024, 5:42 a.m. | Anastasis Kratsios A. Martina Neuman Gudmund Pammer

cs.LG updates on arXiv.org arxiv.org

Many of the foundations of machine learning rely on the idealized premise that all input and output spaces are infinite, e.g.~$\mathbb{R}^d$. This core assumption is systematically violated in practice due to digital computing limitations from finite machine precision, rounding, and limited RAM. In short, digital computers operate on finite grids in $\mathbb{R}^d$. By exploiting these discrete structures, we show the curse of dimensionality in statistical learning is systematically broken when models are implemented on real computers. Consequentially, we obtain new …

computers computing core cs.lg digital dimensionality geometry limitations machine machine learning practice precision spaces the curse of dimensionality via

Research Scholar (Technical Research)

@ Centre for the Governance of AI | Hybrid; Oxford, UK

HPC Engineer (x/f/m) - DACH

@ Meshcapade GmbH | Remote, Germany

Senior Analytics Engineer (Retail)

@ Lightspeed Commerce | Toronto, Ontario, Canada

Data Scientist II, BIA GPS India Operations

@ Bristol Myers Squibb | Hyderabad

Analytics Engineer

@ Bestpass | Remote

Senior Analyst - Data Management

@ Marsh McLennan | Mumbai - Hiranandani