Structured Prediction in Online Learning
<|reference_start|>Structured Prediction in Online Learning: 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.<|reference_end|>
@article{boudart2024structured,
    title={Structured Prediction in Online Learning},
    author={Pierre Boudart (DI-ENS, PSL), Alessandro Rudi (PSL, DI-ENS, Inria),
  Pierre Gaillard (UGA, LJK)},
    journal={arXiv preprint arXiv:2406.12366},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12366},
    primaryClass={cs.LG math.ST stat.ML stat.TH}
}