Provable local learning rule by expert aggregation for a Hawkes network - Archive ouverte HAL
Communication Dans Un Congrès Année : 2024

Provable local learning rule by expert aggregation for a Hawkes network

Résumé

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.
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Dates et versions

hal-04065229 , version 1 (12-04-2023)
hal-04065229 , version 2 (21-02-2024)

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Sophie Jaffard, Samuel Vaiter, Alexandre Muzy, Patricia Reynaud-Bouret. Provable local learning rule by expert aggregation for a Hawkes network. AISTATS 2024 - The 27th International Conference on Artificial Intelligence and Statistics, May 2024, Valence, Spain. ⟨hal-04065229v2⟩
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