We investigate the recently introduced model of learning with improvements, where agents are allowed to make small changes to their feature values to be warranted a more desirable label. We extensively extend previously published results by providing combinatorial dimensions that characterize online learnability in this model, by analyzing the multiclass setup, learnability in a bandit feedback setup, modeling agents' cost for making improvements and more.

翻译：我们研究了最近提出的"伴随改进的学习"模型，该模型允许智能体对其特征值进行微小调整以获得更理想的标签。我们通过以下多方面扩展了已有研究成果：提出刻画该模型在线可学习性的组合维度、分析多分类场景、研究赌博机反馈机制下的可学习性、建模智能体实施改进的成本等。