137 citations · 138 across the 4 of their papers we have counts for
5 papers
OpenAL: Evaluation and Interpretation of Active Learning Strategies
W. Jonas, A. Abraham, L. Dreyfus-Schmidt
Despite the vast body of literature on Active Learning (AL), there is no comprehensive and open benchmark allowing for efficient and simple comparison of proposed samplers. Additio…
Towards Clear Expectations for Uncertainty Estimation
Victor Bouvier, Simona Maggio, Alexandre Abraham +1
If Uncertainty Quantification (UQ) is crucial to achieve trustworthy Machine Learning (ML), most UQ methods suffer from disparate and inconsistent evaluation protocols. We claim th…
Sample Noise Impact on Active Learning
Alexandre Abraham, Léo Dreyfus-Schmidt
This work explores the effect of noisy sample selection in active learning strategies. We show on both synthetic problems and real-life use-cases that knowledge of the sample noise…
Rebuilding Trust in Active Learning with Actionable Metrics
Alexandre Abraham, Léo Dreyfus-Schmidt
Active Learning (AL) is an active domain of research, but is seldom used in the industry despite the pressing needs. This is in part due to a misalignment of objectives, while rese…
Offline A/B testing for Recommender Systems
Alexandre Gilotte, Clément Calauzènes, Thomas Nedelec +2
Before A/B testing online a new version of a recommender system, it is usual to perform some offline evaluations on historical data. We focus on evaluation methods that compute an…