404 citations · 424 across the 9 of their papers we have counts for
10 papers
Linear Programs with Conjunctive Database Queries
Florent Capelli, Nicolas Crosetti, Joachim Niehren +1
In this paper, we study the problem of optimizing a linear program whose variables are the answers to a conjunctive query. For this we propose the language LP(CQ) for specifying li…
An Accurate, Scalable and Verifiable Protocol for Federated Differentially Private Averaging
César Sabater, Aurélien Bellet, Jan Ramon
Learning from data owned by several parties, as in federated learning, raises challenges regarding the privacy guarantees provided to participants and the correctness of the comput…
Solving linear programs on factorized databases
Florent Capelli, Nicolas Crosetti, Joachim Niehren +1
A typical workflow for solving a linear programming problem is to first write a linear program parametrized by the data in a language such as Math GNU Prog or AMPL then call the so…
Hiding in the Crowd: A Massively Distributed Algorithm for Private Averaging with Malicious Adversaries
Pierre Dellenbach, Aurélien Bellet, Jan Ramon
The amount of personal data collected in our everyday interactions with connected devices offers great opportunities for innovative services fueled by machine learning, as well as…
A lower bound on the probability that a binomial random variable is exceeding its mean
Christos Pelekis, Jan Ramon
We provide a lower bound on the probability that a binomial random variable is exceeding its mean. Our proof employs estimates on the mean absolute deviation and the tail condition…
Hoeffding's inequality for sums of weakly dependent random variables
Christos Pelekis, Jan Ramon
We provide a systematic approach to deal with the following problem. Let be, possibly dependent, -valued random variables. What is a sharp upper bound on th…