4 papers
Maximum Likelihood Estimation for Hawkes Processes with self-excitation or inhibition
Anna Bonnet, Miguel Martinez Herrera, Maxime Sangnier
In this paper, we present a maximum likelihood method for estimating the parameters of a univariate Hawkes process with self-excitation or inhibition. Our work generalizes techniqu…
Approximating Lipschitz continuous functions with GroupSort neural networks
Ugo Tanielian, Maxime Sangnier, Gerard Biau
Recent advances in adversarial attacks and Wasserstein GANs have advocated for use of neural networks with restricted Lipschitz constants. Motivated by these observations, we study…
Infinite-Task Learning with RKHSs
Romain Brault, Alex Lambert, Zoltán Szabó +2
Machine learning has witnessed tremendous success in solving tasks depending on a single hyperparameter. When considering simultaneously a finite number of tasks, multi-task learni…
Some Theoretical Properties of GANs
G. Biau, B. Cadre, M. Sangnier +1
Generative Adversarial Networks (GANs) are a class of generative algorithms that have been shown to produce state-of-the art samples, especially in the domain of image creation. Th…