machine learning ↔ robustness

Relations (1)

related 2.00 — strongly supporting 3 facts

Robustness is a core concept within machine learning, as evidenced by theoretical frameworks [1] and extensive historical analysis in the field [2]. Furthermore, modern research continues to grapple with formalizing robustness specifically within the context of evolving machine learning architectures [3].

Facts (3)

Sources

A Survey on the Theory and Mechanism of Large Language Models arxiv.org arXiv 2 facts

claimThe current landscape of large language models presents new challenges for defining and formalizing concepts like 'robustness', 'fairness', and 'privacy' compared to traditional machine learning, as noted by Chang et al. (2024), Anwar et al. (2024), Dominguez-Olmedo et al. (2025), and Hardt and Mendler-Dünner (2025).

claimTraditional machine learning literature extensively analyzed robustness (Muravev and Petiushko, 2021; Ruan et al., 2021), fairness (Kleinberg et al., 2016; Liu et al., 2019), and privacy (Li et al., 2017; Kairouz et al., 2015) because these concepts were well-defined and formalizable using precise mathematical objectives.

A Comprehensive Review of Neuro-symbolic AI for Robustness ... link.springer.com Springer 1 fact

referenceFreiesleben and Grote (2023) propose a theory of robustness in machine learning that extends beyond simple generalization.