Many real-world learning scenarios face the challenge of slow concept drift, where data distributions change gradually over time. In this setting, we pose the problem of learning temporally sensitive importance weights for training data, in order to optimize predictive accuracy. We propose a class of temporal reweighting functions that can capture multiple timescales of change in the data, as well as instance-specific characteristics. We formulate a bi-level optimization criterion, and an associated meta-learning algorithm, by which these weights can be learned. In particular, our formulation trains an auxiliary network to output weights as a function of training instances, thereby compactly representing the instance weights. We validate our temporal reweighting scheme on a large real-world dataset of 39M images spread over a 9 year period. Our extensive experiments demonstrate the necessity of instance-based temporal reweighting in the dataset, and achieve significant improvements to classical batch-learning approaches. Further, our proposal easily generalizes to a streaming setting and shows significant gains compared to recent continual learning methods.

翻译：许多现实世界的学习情景面临着缓慢的概念漂移的挑战,即数据分配随着时间推移而逐渐变化。在这个背景下,我们提出了学习培训数据的时间敏感重要重量的问题,以便优化预测准确性。我们建议了一组时间重加权功能,可以捕捉数据变化的多重时间尺度,以及具体实例的特点。我们制定了双级优化标准和相关的元学习算法,从而可以学习这些加权。特别是,我们的配方将输出重量的辅助网络训练成培训实例的函数,从而缩略地代表实例重量。我们验证了在9年期间分布在39M图像的大型真实世界数据集上的时间重加权计划。我们的广泛实验表明,有必要根据实例对数据集进行时间重加权,并大大改进典型的批量学习方法。此外,我们的提案很容易概括成流学环境,并显示与最近的持续学习方法相比所取得的重大收益。