17 citations · 46 across the 6 of their papers we have counts for
7 papers
Inference of multivariate exponential Hawkes processes with inhibition and application to neuronal activity
Anna Bonnet, Miguel Martinez Herrera, Maxime Sangnier
The multivariate Hawkes process is a past-dependent point process used to model the relationship of event occurrences between different phenomena.Although the Hawkes process was or…
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…
Some Theoretical Insights into Wasserstein GANs
Gérard Biau, Maxime Sangnier, Ugo Tanielian
Generative Adversarial Networks (GANs) have been successful in producing outstanding results in areas as diverse as image, video, and text generation. Building on these successes,…
Proximal boosting: aggregating weak learners to minimize non-differentiable losses
Erwan Fouillen, Claire Boyer, Maxime Sangnier
Gradient boosting is a prediction method that iteratively combines weak learners to produce a complex and accurate model. From an optimization point of view, the learning procedure…
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…