Provable local learning rule by expert aggregation for a Hawkes network
Statistics Theory
2024-02-26 v2 Statistics Theory
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.
Cite
@article{arxiv.2304.08061,
title = {Provable local learning rule by expert aggregation for a Hawkes network},
author = {Sophie Jaffard and Samuel Vaiter and Alexandre Muzy and Patricia Reynaud-Bouret},
journal= {arXiv preprint arXiv:2304.08061},
year = {2024}
}
Comments
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (AISTATS), In press