5 papers
On the Calibration of Crowdsourcing Aggregators
Gabriel Singer, Reda Arab, Maïssa Frikha +4
Calibration has been extensively studied for classifiers, but remains largely unexplored for crowdsourcing aggregation methods, which are increasingly used for making probabilistic…
Extreme Binary Classification: Extreme Value Theory for Extreme Constraint on False Negative
Samuel Gruffaz, Muhammad Fawad, Jaakko Nevalainen
While binary classification is one of the most extensively studied problems in machine learning, the regime in which the goal is to learn a classifier with an almost zero false neg…
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…
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…
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…