no code implementations • 10 Apr 2024 • Rachid Zeghlache, Pierre-Henri Conze, Mostafa El Habib Daho, Yihao Li, Hugo Le Boité, Ramin Tadayoni, Pascal Massin, Béatrice Cochener, Alireza Rezaei, Ikram Brahim, Gwenolé Quellec, Mathieu Lamard
This work proposes a novel framework for analyzing disease progression using time-aware neural ordinary differential equations (NODE).
no code implementations • 24 Mar 2024 • Rachid Zeghlache, Pierre-Henri Conze, Mostafa El Habib Daho, Yihao Li, Alireza Rezaei, Hugo Le Boité, Ramin Tadayoni, Pascal Massin, Béatrice Cochener, Ikram Brahim, Gwenolé Quellec, Mathieu Lamard
Our results demonstrated the relevancy of both time-aware position embedding and masking strategies based on disease progression knowledge.
no code implementations • 6 Jul 2019 • Piotr Indyk, Sepideh Mahabadi, Shayan Oveis Gharan, Alireza Rezaei
In this work, first we provide a theoretical approximation guarantee of $O(C^{k^2})$ for the Greedy algorithm in the context of composable core-sets; Further, we propose to use a Local Search based algorithm that while being still practical, achieves a nearly optimal approximation bound of $O(k)^{2k}$; Finally, we implement all three algorithms and show the effectiveness of our proposed algorithm on standard data sets.
no code implementations • 20 Oct 2018 • Shayan Oveis Gharan, Alireza Rezaei
We study the Gibbs sampling algorithm for continuous determinantal point processes.
no code implementations • 31 Jul 2018 • Piotr Indyk, Sepideh Mahabadi, Shayan Oveis Gharan, Alireza Rezaei
We show that for many objective functions one can use a spectral spanner, independent of the underlying functions, as a core-set and obtain almost optimal composable core-sets.
no code implementations • 16 Feb 2016 • Nima Anari, Shayan Oveis Gharan, Alireza Rezaei
Strongly Rayleigh distributions are natural generalizations of product and determinantal probability distributions and satisfy strongest form of negative dependence properties.