AISTATS 2024poster1 citations
Provable local learning rule by expert aggregation for a Hawkes network
Sophie Jaffard, Samuel Vaiter, Alexandre Muzy, Patricia Reynaud-Bouret
Abstract
We propose a simple network of Hawkes processes as a cognitive model capable of learning to classify objects. Our learning algorithm, named HAN for Hawkes Aggregation of Neurons, is based on a local synaptic learning rule based on spiking probabilities at each output node. We were able to use local regret bounds to prove mathematically that the network is able to learn on average and even asymptotically under more restrictive assumptions.
BibTeX
@InProceedings{pmlr-v238-jaffard24a,
title = {Provable local learning rule by expert aggregation for a {H}awkes network},
author = {Jaffard, Sophie and Vaiter, Samuel and Muzy, Alexandre and Reynaud-Bouret, Patricia},
booktitle = {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics},
pages = {1837--1845},
year = {2024},
editor = {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen},
volume = {238},
series = {Proceedings of Machine Learning Research},
month = {02--04 May},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v238/jaffard24a/jaffard24a.pdf},
url = {https://proceedings.mlr.press/v238/jaffard24a.html},
abstract = {We propose a simple network of Hawkes processes as a cognitive model capable of learning to classify objects. Our learning algorithm, named HAN for Hawkes Aggregation of Neurons, is based on a local synaptic learning rule based on spiking probabilities at each output node. We were able to use local regret bounds to prove mathematically that the network is able to learn on average and even asymptotically under more restrictive assumptions.}
}