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Aris Daniilidis, Laurent Miclo, and David Salas
vol. 287, n. 11, 2024, 54 pages
Laurent Miclo
vol. 29, n. 76, 2024, pp. 1–12
Marc Arnaudon, Koléhè Coulibaly-Pasquier, and Laurent Miclo
vol. 29, 2024, pp. 1–43
Sébastien Gadat, and Clément Lalanne
vol. 235, “ICML'24: Proceedings of the 41st International Conference on Machine Learning”, 2024, pp. 25936 – 25975
Fueled by the ever-increasing need for statistics that guarantee the privacy of their training sets, this article studies the centrally-private estimation of Sobolev-smooth densities of probability over the hypercube in dimension d. The contributions of this article are two-fold : Firstly, it...
David Martimort, and Jérôme Pouyet
2024, forthcoming
Pay-TV firms compete both downstream to attract viewers and upstream to acquire broadcasting rights. Because profits inherited from downstream competition satisfy a Convexity Property, allocating rights to the dominant firm maximizes the industry profit. Such an exclusive allocation of rights...
Jean-Bernard Lasserre
2024
vol. 362, 2024, pp. 1455–1473
Jean-Bernard Lasserre, and Yuan Xu
Yukai Tang, Jean-Bernard Lasserre, and Heng Yang
vol. 242, 2024, pp. 286–-298
Set-membership estimation (SME) outputs a set estimator that guarantees to cover the groundtruth. Such sets are, however, defined by (many) abstract (and potentially nonconvex) constraints and therefore difficult to manipulate. We present tractable algorithms to compute simple and tight...
