Offline A/B testing for Recommender Systems
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 estimator of the...
Sample Noise Impact on Active Learning
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 can...
Towards Clear Expectations for Uncertainty Estimation
If Uncertainty Quantification (UQ) is crucial to achieve trustworthy Machine Learning (ML), most UQ methods suffer from disparate and inconsistent evaluation protocols. We claim this inconsistency...
An in silico drug repurposing pipeline to identify drugs with the potential to inhibit SARS-CoV-2 replication
Drug repurposing provides an opportunity to redeploy drugs, which ideally are already approved for use in humans, for the treatment of other diseases. For example, the repurposing of dexamethasone...
Rebuilding Trust in Active Learning with Actionable Metrics
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 research strives at...
Machine Learning for Neuroimaging with Scikit-Learn
Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate analysis of...
Region segmentation for sparse decompositions: better brain parcellations from rest fMRI
Functional Magnetic Resonance Images acquired during resting-state provide information about the functional organization of the brain through measuring correlations between brain areas....
Improving Bias Correction Standards by Quantifying Its Effects on Treatment Outcomes
Extracting Brain Regions from Rest fMRI with Total-Variation Constrained Dictionary Learning
Integrating Multimodal Priors in Predictive Models for the Functional Characterization of Alzheimer's Disease
Improving Bias Correction Standards by Quantifying its Effects on Treatment Outcomes
With the growing access to administrative health databases, retrospective studies have become crucial evidence for medical treatments. Yet, non-randomized studies frequently face selection biases,...