18 citations · 43 across the 6 of their papers we have counts for
10 papers
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…
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.…
On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent
Scott Pesme, Aymeric Dieuleveut, Nicolas Flammarion
Constant step-size Stochastic Gradient Descent exhibits two phases: a transient phase during which iterates make fast progress towards the optimum, followed by a stationary phase d…
Debiasing Stochastic Gradient Descent to handle missing values
Julie Josse, Aude Sportisse, Claire Boyer +1
Stochastic gradient algorithm is a key ingredient of many machine learning methods, particularly appropriate for large-scale learning.However, a major caveat of large data is their…