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Pré-Publication, Document De Travail Année : 2024

Structured Prediction in Online Learning

Résumé

We study a theoretical and algorithmic framework for structured prediction in the online learning setting. The problem of structured prediction, i.e. estimating function where the output space lacks a vectorial structure, is well studied in the literature of supervised statistical learning. We show that our algorithm is a generalisation of optimal algorithms from the supervised learning setting, and achieves the same excess risk upper bound also when data are not i.i.d. Moreover, we consider a second algorithm designed especially for non-stationary data distributions, including adversarial data. We bound its stochastic regret in function of the variation of the data distributions.
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hal-04614901 , version 1 (17-06-2024)

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  • HAL Id : hal-04614901 , version 1

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Pierre Boudart, Alessandro Rudi, Pierre Gaillard. Structured Prediction in Online Learning. 2024. ⟨hal-04614901⟩
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