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Benedikt Höltgen

2 accepted papers

2025

Position: The Categorization of Race in ML is a Flawed Premise

ICML 2025spotlight

This position paper critiques the reliance on rigid racial taxonomies in machine learning, exposing their U.S.-centric nature and lack of global applicability—particularly in Europe, where race categories are not commonly used. These classifications oversimplify racial identity, erasing the experien…

Cited by 0SourcePDFScholar
2022

Prioritized Training on Points that are Learnable, Worth Learning, and not yet Learnt

ICML 2022spotlight

Training on web-scale data can take months. But much computation and time is wasted on redundant and noisy points that are already learnt or not learnable. To accelerate training, we introduce Reducible Holdout Loss Selection (RHO-LOSS), a simple but principled technique which selects approximately…