1 citations · 2 across the 6 of their papers we have counts for
8 papers
Adaptive Personalized Federated Learning via Multi-task Averaging of Kernel Mean Embeddings
Jean-Baptiste Fermanian, Batiste Le Bars, Aurélien Bellet
Personalized Federated Learning (PFL) enables a collection of agents to collaboratively learn individual models without sharing raw data. We propose a new PFL approach in which eac…
Class conditional conformal prediction for multiple inputs by p-value aggregation
Jean-Baptiste Fermanian, Mohamed Hebiri, Joseph Salmon
Conformal prediction methods are statistical tools designed to quantify uncertainty and generate predictive sets with guaranteed coverage probabilities. This work introduces an inn…
Conformal Prediction for Long-Tailed Classification
Tiffany Ding, Jean-Baptiste Fermanian, Joseph Salmon
Many real-world classification problems, such as plant identification, have extremely long-tailed class distributions. In order for prediction sets to be useful in such settings, t…
Transductive Conformal Inference for Full Ranking
Jean-Baptiste Fermanian, Pierre Humbert, Gilles Blanchard
We introduce a method based on Conformal Prediction (CP) to quantify the uncertainty of full ranking algorithms. We focus on a specific scenario where items are to be ranked…
Estimation of multiple mean vectors in high dimension
Gilles Blanchard, Jean-Baptiste Fermanian, Hannah Marienwald
We endeavour to estimate numerous multi-dimensional means of various probability distributions on a common space based on independent samples. Our approach involves forming estimat…
Nonasymptotic one-and two-sample tests in high dimension with unknown covariance structure
Gilles Blanchard, Jean-Baptiste Fermanian
Let be an i.i.d. sample of square-integrable variables in , \GB{with common expectation and covariance matrix , both unkn…