Provable local learning rule by expert aggregation for a Hawkes network

Sophie Jaffard, Samuel Vaiter, Alexandre Muzy, Patricia Reynaud-Bouret
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, PMLR 238:1837-1845, 2024.

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 this Paper

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.} }
Endnote
%0 Conference Paper %T Provable local learning rule by expert aggregation for a Hawkes network %A Sophie Jaffard %A Samuel Vaiter %A Alexandre Muzy %A Patricia Reynaud-Bouret %B Proceedings of The 27th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2024 %E Sanjoy Dasgupta %E Stephan Mandt %E Yingzhen Li %F pmlr-v238-jaffard24a %I PMLR %P 1837--1845 %U https://proceedings.mlr.press/v238/jaffard24a.html %V 238 %X 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.
APA
Jaffard, S., Vaiter, S., Muzy, A. & Reynaud-Bouret, P.. (2024). Provable local learning rule by expert aggregation for a Hawkes network. Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 238:1837-1845 Available from https://proceedings.mlr.press/v238/jaffard24a.html.

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