Discriminative Feature Feedback is a setting proposed by Dastupta et al. (2018), which provides a protocol for interactive learning based on feature explanations that are provided by a human teacher. The features distinguish between the labels of pairs of possibly similar instances. That work has shown that learning in this model can have considerable statistical and computational advantages over learning in standard label-based interactive learning models. In this work, we provide new robust interactive learning algorithms for the Discriminative Feature Feedback model, with mistake bounds that are significantly lower than those of previous robust algorithms for this setting. In the adversarial setting, we reduce the dependence on the number of protocol exceptions from quadratic to linear. In addition, we provide an algorithm for a slightly more restricted model, which obtains an even smaller mistake bound for large models with many exceptions. In the stochastic setting, we provide the first algorithm that converges to the exception rate with a polynomial sample complexity. Our algorithm and analysis for the stochastic setting involve a new construction that we call Feature Influence, which may be of wider applicability.

翻译：判别特征反馈是Dastupta等人(2018)提出的一种交互式学习设置，该设置基于人类教师提供的特征解释建立了一种交互式学习协议。这些特征用于区分可能相似实例对的标签。已有研究表明，与基于标准标签的交互式学习模型相比，该模型下的学习在统计和计算方面具有显著优势。本文针对判别特征反馈模型提出了新的鲁棒交互式学习算法，其错误界限显著低于该设置下先前的鲁棒算法。在对抗性设置中，我们将协议异常次数的依赖关系从二次降低至线性。此外，我们为一种略受限制的模型提供了算法，该算法在存在大量异常的大型模型上获得了更小的错误界限。在随机设置中，我们首次提出了能以多项式样本复杂度收敛到异常率的算法。针对随机设置，我们的算法与分析涉及一种称为"特征影响"的新构造，该构造可能具有更广泛的适用性。