Codex Ethicae Artificiality Yousefi Arash Yousefi (2025)

This technical note presents the original formulation of the Unified Multi-Objective Loss Function developed by Arash Yousefi (2025), with SHA256 fingerprint for authentication and verification. Hash (SHA256): `e201c8ff6976fcdcf2d35eea365889d321df8af4d2181b9a350aad17f2d07bfe` The loss function aims to balance fairness, robustness, explainability, and sparsity using a KKT-constrained optimization framework. This version is formally timestamped, DOI-verified, and may be cited as intellectual property of Arash Yousefi.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-16
DOI
https://doi.org/10.5281/zenodo.15621941
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
0.00
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article

Codex Ethicae Artificiality Yousefi Arash Yousefi (2025)

Zenodo (CERN European Organization for Nuclear Research)
Adversarial Robustness in Machine Learning
article

Codex Ethicae Artificiality Yousefi Arash Yousefi (2025)

article en

Abstract

This technical note presents the original formulation of the Unified Multi-Objective Loss Function developed by Arash Yousefi (2025), with SHA256 fingerprint for authentication and verification. Hash (SHA256): `e201c8ff6976fcdcf2d35eea365889d321df8af4d2181b9a350aad17f2d07bfe` The loss function aims to balance fairness, robustness, explainability, and sparsity using a KKT-constrained optimization framework. This version is formally timestamped, DOI-verified, and may be cited as intellectual property of Arash Yousefi.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 75%
Adversarial Robustness in Machine Learning
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Codex Ethicae Artificiality Yousefi Arash Yousefi (2025) · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS