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.

翻译：我们研究在线学习环境下结构化预测的理论与算法框架。结构化预测问题，即估计输出空间缺乏向量结构的函数，在监督统计学习文献中已得到充分研究。我们证明，所提出的算法是监督学习场景中已有最优算法的推广，即使在数据非独立同分布的情况下，也能达到相同的超额风险上界。此外，我们设计了第二种专门针对非平稳数据分布（包括对抗性数据）的算法，并以数据分布的变化量函数界定了其随机遗憾。