论文标题
估计平滑：一个简单的基线，用于学习辅助信息
Post-Estimation Smoothing: A Simple Baseline for Learning with Side Information
论文作者
论文摘要
观察数据通常伴随着自然的结构指标，例如时间戳记或地理位置，这对预测任务有意义，但经常被丢弃。我们利用语义上有意义的索引数据，同时确保对潜在的非信息或误导性指数的鲁棒性。我们建议将估计平滑操作员作为将结构指数数据纳入预测的快速有效方法。由于平滑步骤与原始预测指标分开，因此它适用于一系列的机器学习任务，而无需重新训练模型。我们的理论分析详细介绍了简单的条件，在这些条件下，估计平滑将提高与原始预测因子相比的准确性。我们对大规模空间和时间数据集进行的实验突出了实践中估计平滑的速度和准确性。这些结果共同阐明了一种新颖的方式，可以在机器学习中考虑和结合索引变量的自然结构。
Observational data are often accompanied by natural structural indices, such as time stamps or geographic locations, which are meaningful to prediction tasks but are often discarded. We leverage semantically meaningful indexing data while ensuring robustness to potentially uninformative or misleading indices. We propose a post-estimation smoothing operator as a fast and effective method for incorporating structural index data into prediction. Because the smoothing step is separate from the original predictor, it applies to a broad class of machine learning tasks, with no need to retrain models. Our theoretical analysis details simple conditions under which post-estimation smoothing will improve accuracy over that of the original predictor. Our experiments on large scale spatial and temporal datasets highlight the speed and accuracy of post-estimation smoothing in practice. Together, these results illuminate a novel way to consider and incorporate the natural structure of index variables in machine learning.
