A Model for Imbalanced Label Aggregation: A Focus on Minority-Class Detection

arXiv:2607.24622 · stat.ML, cs.LG · Submitted 2026-07-27 · Read on arXiv

Gabriel Singer, Samuel Gruffaz, Olivier Vo Van, Nicolas Vayatis, Argyris Kalogeratos

stat.ML, cs.LG

Submitted: 2026-07-27

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 13× in this paper · explained in 1 episode
• Class Imbalance — 13× in this paper · explained in 3 episodes
• Benchmark — 11× in this paper · explained in 2 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Latent Variables — 3× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 6 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Composition — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 14 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Recognizing Textual Entailment — 1× in this paper
• Redundancy — 1× in this paper · explained in 2 episodes

Sources

• Human uncertainty makes classification more robust
• Optimal Fair Aggregation of Crowdsourced Noisy Labels using Demographic Parity Constraints

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