Indistinguishable predictions and multi-group fair learning

DOI10.1007/978-3-031-30545-0_1OpenAlexW4365935196MaRDI QIDQ6138076

Guy N. Rothblum

Publication date: 16 January 2024

Published in: Advances in Cryptology – EUROCRYPT 2023 (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-031-30545-0_1

zbMATH Keywords

computational indistinguishabilityprediction algorithmsoutcome indistinguishability

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Cites Work

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• Toward efficient agnostic learning
• The reproducible properties of correct forecasts
• On individual risk
• An easier way to calibrate.
• Fairness through awareness
• Pseudorandomness
• Can theories be tested?
• The Complexity of Forecast Testing
• Foundations of Cryptography
• The Well-Calibrated Bayesian
• Asymptotic calibration
• Calibration with Many Checking Rules
• Advancing subgroup fairness via sleeping experts
• Computational Complexity
• Outcome indistinguishability

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