Structured Prediction in Online Learning

Pierre Boudart; Alessandro Rudi; Pierre Gaillard

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arXiv · 2406.12366

Structured Prediction in Online Learning

Pierre Boudart·Alessandro Rudi·Pierre Gaillard

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Abstract

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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Pierre Boudart, Alessandro Rudi, Pierre Gaillard. 2024-06-18. Structured Prediction in Online Learning. https://arxiv.org/abs/2406.12366

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