232 citations · 453 across the 44 of their papers we have counts for
6 papers · 1 filter
Quadratic minimization: from conjugate gradient to an adaptive Heavy-ball method with Polyak step-sizes
Baptiste Goujaud, Adrien Taylor, Aymeric Dieuleveut
In this work, we propose an adaptive variation on the classical Heavy-ball method for convex quadratic minimization. The adaptivity crucially relies on so-called "Polyak step-sizes…
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers +21
Federated Learning (FL) is a novel approach enabling several clients holding sensitive data to collaboratively train machine learning models, without centralizing data. The cross-s…
Optimal first-order methods for convex functions with a quadratic upper bound
Baptiste Goujaud, Adrien Taylor, Aymeric Dieuleveut
We analyze worst-case convergence guarantees of first-order optimization methods over a function class extending that of smooth and convex functions. This class contains convex fun…
Adaptive Conformal Predictions for Time Series
Margaux Zaffran, Aymeric Dieuleveut, Olivier Féron +2
Uncertainty quantification of predictive models is crucial in decision-making problems. Conformal prediction is a general and theoretically sound answer. However, it requires excha…
Minimax rate of consistency for linear models with missing values
Alexis Ayme, Claire Boyer, Aymeric Dieuleveut +1
Missing values arise in most real-world data sets due to the aggregation of multiple sources and intrinsically missing information (sensor failure, unanswered questions in surveys.…
PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python
Baptiste Goujaud, Céline Moucer, François Glineur +3
PEPit is a Python package aiming at simplifying the access to worst-case analyses of a large family of first-order optimization methods possibly involving gradient, projection, pro…