3 papers
stat.ML2026
A Model for Imbalanced Label Aggregation: A Focus on Minority-Class Detection
Gabriel Singer, Samuel Gruffaz, Olivier Vo Van +2
We study imbalanced crowdsourcing with a focus on class-dependent annotator accuracy, a setting that, to the best of our knowledge, remains relatively underexplored despite its imp…
cs.LG2026
Optimal Fair Aggregation of Crowdsourced Noisy Labels using Demographic Parity Constraints
Gabriel Singer, Samuel Gruffaz, Olivier Vo Van +2
As acquiring reliable ground-truth labels is usually costly, or infeasible, crowdsourcing and aggregation of noisy human annotations is the typical resort. Aggregating subjective l…
math.OC2026
Enhancing Exploration in Global Optimization by Noise Injection in the Probability Measures Space
Gaëtan Serré, Pierre Germain, Samuel Gruffaz +1
McKean-Vlasov (MKV) systems provide a unifying framework for recent state-of-the-art particlebased methods for global optimization. While individual particles follow stochastic tra…