6 citations · 11 across the 12 of their papers we have counts for
12 papers
Flexibility allocation in random bipartite matching markets: exact matching rates and dominance regimes
Taha Ameen, Flore Sentenac, Sophie H. Yu
This paper studies how a fixed flexibility budget should be allocated across the two sides of a balanced bipartite matching market. We model compatibilities via a sparse bipartite…
On the Average-Case Performance of Greedy for Maximum Coverage
Eric Balkanski, Jason Chatzitheodorou, Flore Sentenac
For the classical maximum coverage problem, the greedy algorithm achieves a worst-case approximation, which is optimal unless . The notion of coverage…
A uniformity principle for spatial matching
Taha Ameen, Flore Sentenac, Sophie H. Yu
Platforms matching spatially distributed supply to demand face a fundamental design choice: given a fixed total budget of service range, how should it be allocated across supply no…
The Challenger: When Do New Data Sources Justify Switching Machine Learning Models?
Vassilis Digalakis, Christophe Pérignon, Sébastien Saurin +1
Organizations often have an incumbent predictive model in production when new data sources become available. Because historical training data lack the new features, a challenger mo…
Balancing optimism and pessimism in offline-to-online learning
Flore Sentenac, Ilbin Lee, Csaba Szepesvari
We consider what we call the offline-to-online learning setting, focusing on stochastic finite-armed bandit problems. In offline-to-online learning, a learner starts with offline d…
Almost Free: Self-concordance in Natural Exponential Families and an Application to Bandits
Shuai Liu, Alex Ayoub, Flore Sentenac +2
We prove that single-parameter natural exponential families with subexponential tails are self-concordant with polynomial-sized parameters. For subgaussian natural exponential fami…
Online Matching in Geometric Random Graphs
Flore Sentenac, Nathan Noiry, Matthieu Lerasle +2
We investigate online maximum cardinality matching, a central problem in ad allocation. In this problem, users are revealed sequentially, and each new user can be paired with any p…
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…
Robust Estimation of Discrete Distributions under Local Differential Privacy
Julien Chhor, Flore Sentenac
Although robust learning and local differential privacy are both widely studied fields of research, combining the two settings is just starting to be explored. We consider the prob…
Pure Exploration and Regret Minimization in Matching Bandits
Flore Sentenac, Jialin Yi, Clément Calauzènes +2
Finding an optimal matching in a weighted graph is a standard combinatorial problem. We consider its semi-bandit version where either a pair or a full matching is sampled sequentia…
Online Matching in Sparse Random Graphs: Non-Asymptotic Performances of Greedy Algorithm
Nathan Noiry, Flore Sentenac, Vianney Perchet
Motivated by sequential budgeted allocation problems, we investigate online matching problems where connections between vertices are not i.i.d., but they have fixed degree distribu…
Decentralized Learning in Online Queuing Systems
Flore Sentenac, Etienne Boursier, Vianney Perchet
Motivated by packet routing in computer networks, online queuing systems are composed of queues receiving packets at different rates. Repeatedly, they send packets to servers, each…