742 citations
- Institut national de recherche en sciences et technologies du numériqueFR141 papers
- École PolytechniqueFR65 papers
- Centre National de la Recherche ScientifiqueFR37 papers
- Université Paris-SaclayFR30 papers
- Laboratoire de Recherche en InformatiqueFR24 papers
- Commissariat à l'Énergie Atomique et aux Énergies AlternativesFR16 papers
- Laboratoire de Mathématiques d'OrsayFR14 papers
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12 papers · 1 filter
Demographic parity in regression and classification within the unawareness framework
Vincent Divol, Solenne Gaucher
This paper explores the theoretical foundations of fair regression under the constraint of demographic parity within the unawareness framework, where disparate treatment is prohibi…
Resampling and averaging coordinates on data
Andrew J. Blumberg, Mathieu Carriere, Jun Hou Fung +1
We introduce algorithms for robustly computing intrinsic coordinates on point clouds. Our approach relies on generating many candidate coordinates by subsampling the data and varyi…
Geodesic Optimization for Predictive Shift Adaptation on EEG data
Apolline Mellot, Antoine Collas, Sylvain Chevallier +2
Electroencephalography (EEG) data is often collected from diverse contexts involving different populations and EEG devices. This variability can induce distribution shifts in the d…
Adaptive Multi-View ICA: Estimation of noise levels for optimal inference
Hugo Richard, Pierre Ablin, Aapo Hyvärinen +2
We consider a multi-view learning problem known as group independent component analysis (group ICA), where the goal is to recover shared independent sources from many views. The st…
High-Dimensional Multi-Task Averaging and Application to Kernel Mean Embedding
Hannah Marienwald, Jean-Baptiste Fermanian, Gilles Blanchard
We propose an improved estimator for the multi-task averaging problem, whose goal is the joint estimation of the means of multiple distributions using separate, independent data se…
Spatio-Temporal Alignments: Optimal transport through space and time
Hicham Janati, Marco Cuturi, Alexandre Gramfort
Comparing data defined over space and time is notoriously hard, because it involves quantifying both spatial and temporal variability, while at the same time taking into account th…