From the 1 of 4 linked papers with an AI index.
4 papers
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…
Parallel gradient boosting for flexible estimation of conditional distributions
Rémy Chapelle, Nicolas Vayatis, Bruno Falissard +1
The paper introduces parallel gradient boosting, a modification of gradient boosting that uses a single shared descent direction to train one base model per iteration for all targe…
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…
GLOBe: A Modular Global Optimization library
Gaëtan Serré, Argyris Kalogeratos, Nicolas Vayatis
Open-source libraries are have a catalytic role in research pipelines, where new methods must be compared against up-to-date baselines. We present the GLobal Optimization Benchmark…