With the explosion of massive, widely available unlabeled data in the past years, finding label and time efficient, robust learning algorithms has become ever more important in theory and in practice. We study the paradigm of active learning, in which algorithms with access to large pools of data may adaptively choose what samples to label in the hope of exponentially increasing efficiency. By introducing comparisons, an additional type of query comparing two points, we provide the first time and query efficient algorithms for learning non-homogeneous linear separators robust to bounded (Massart) noise. We further provide algorithms for a generalization of the popular Tsybakov low noise condition, and show how comparisons provide a strong reliability guarantee that is often impractical or impossible with only labels - returning a classifier that makes no errors with high probability.

中文翻译：

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具有比较查询的抗噪、可靠的主动分类

随着过去几年海量、广泛可用的未标记数据的爆炸式增长，寻找标签和时间高效、鲁棒的学习算法在理论和实践中变得越来越重要。我们研究了主动学习的范式，在这种范式中，可以访问大量数据的算法可以自适应地选择要标记的样本，以期以指数方式提高效率。通过引入比较，一种比较两点的附加查询类型，我们提供了第一次和查询有效的算法，用于学习对有界（Massart）噪声鲁棒的非齐次线性分隔符。我们进一步提供了流行的 Tsybakov 低噪声条件的泛化算法，