4 citations · 5 across the 9 of their papers we have counts for
12 papers
Optimal Online Stochastic Matching
Paul Duetting, Michal Feldman, Mathieu Molina
We resolve the problem of unweighted online stochastic matching with a known arrival law by establishing its optimal competitive ratio. For Poisson arrivals, we achieve $1-e/e^e\ap…
Near-Optimal Acceleration for Smooth / Nondual Convex First-Order Oracle Optimization
David Martínez-Rubio, Brian Bullins, Cristóbal Guzmán +1
We study the optimization of convex objectives with -Hölder-continuous gradients in over , . (MG26) provides selectors with a movement bound fo…
Fair Prophets
Paul Duetting, Michal Feldman, Mathieu Molina
We initiate the study of -fair prophet inequalities. This interpolates between utilitarian welfare , Nash welfare , and Rawlsian max-min fairness . G…
The First-Order Oracle Complexity of Lipschitz Convex Optimization in Nondual Settings
David Martínez-Rubio, Brian Bullins, Cristóbal Guzmán +1
We study first-order black-box convex optimization over an -ball for objectives Lipschitz in the -norm, solving in the affirmative the nonsmooth version of the COLT…
Kernel Methods for Refined Prophet Inequalities
Patrick Loiseau, Mathieu Molina, Vianney Perchet +2
The single-selection prophet inequality is a canonical Bayesian online selection problem in which independent nonnegative values arrive sequentially and the decision-maker must irr…
Prophet Inequalities under Local Differential Privacy
Achraf Azize, Mathieu Molina, Hugo Richard +1
Many online decision platforms, from hiring marketplaces to auctions, face a tension between efficient decision-making and the protection of participants' privacy. Personal informa…
Tight Sample Complexity for Low-Degree and Sparse Boolean Polynomials
Jasper van Doornmalen, Mathieu Molina, Victor Verdugo +1
Motivated by the optimization of bounded binary black-box functions, we study the problem of learning polynomial surrogates over the Boolean hypercube. To ensure that optimizing th…
Prophet Inequalities: Competing with the Top Items is Easy
Mathieu Molina, Nicolas Gast, Patrick Loiseau +1
We explore a prophet inequality problem, where the values of a sequence of items are drawn i.i.d. from some distribution, and an online decision maker must select one item irrevoca…
The Price of Opportunity Fairness in Matroid Allocation Problems
Rémi Castera, Felipe Garrido-Lucero, Patrick Loiseau +3
We consider matroid allocation problems under opportunity fairness constraints: resources need to be allocated to a set of agents under matroid constraints (which include classical…
Trading-off price for data quality to achieve fair online allocation
Mathieu Molina, Nicolas Gast, Patrick Loiseau +1
We consider the problem of online allocation subject to a long-term fairness penalty. Contrary to existing works, however, we do not assume that the decision-maker observes the pro…
Bounding and Approximating Intersectional Fairness through Marginal Fairness
Mathieu Molina, Patrick Loiseau
Discrimination in machine learning often arises along multiple dimensions (a.k.a. protected attributes); it is then desirable to ensure \emph{intersectional fairness} -- i.e., that…
On Preemption and Learning in Stochastic Scheduling
Nadav Merlis, Hugo Richard, Flore Sentenac +3
We study single-machine scheduling of jobs, each belonging to a job type that determines its duration distribution. We start by analyzing the scenario where the type characteristic…