We develop and analyze a general technique for learning with an unknown distribution drift. Given a sequence of independent observations from the last $T$ steps of a drifting distribution, our algorithm agnostically learns a family of functions with respect to the current distribution at time $T$. Unlike previous work, our technique does not require prior knowledge about the magnitude of the drift. Instead, the algorithm adapts to the sample data. Without explicitly estimating the drift, the algorithm learns a family of functions with almost the same error as a learning algorithm that knows the magnitude of the drift in advance. Furthermore, since our algorithm adapts to the data, it can guarantee a better learning error than an algorithm that relies on loose bounds on the drift.

翻译：我们开发并分析了一种用于应对未知分布漂移的通用学习技术。给定一个漂移分布在最近 $T$ 步中的独立观测序列，我们的算法在不知晓漂移幅度的情况下，能够以近似最优误差学习一族函数，使其适应于时间 $T$ 时的当前分布。与先前研究不同，本技术无需预先知晓漂移的剧烈程度，而是通过样本数据自适应调整。该算法在不显式估计漂移的前提下，其学习误差几乎等同于已知漂移幅度的预设学习算法。此外，由于算法对数据具有自适应性，它能够保证比依赖漂移宽松边界的算法获得更优的学习误差。