标题： 后估计平滑：利用辅助信息学习的简单基线 显示英文标题

标题： Post-Estimation Smoothing: A Simple Baseline for Learning with Side Information

摘要： 观测数据通常伴随着自然的结构索引，如时间戳或地理位置，这些对预测任务是有意义的，但常常被丢弃。 我们利用语义上有意义的索引数据，同时确保对可能无信息或具有误导性的索引具有鲁棒性。 我们提出一种后估计平滑算子，作为一种快速有效的方法，将结构索引数据纳入预测中。 由于平滑步骤与原始预测器分离，它适用于广泛的机器学习任务，无需重新训练模型。 我们的理论分析详细说明了后估计平滑在何种简单条件下可以提高准确率。 我们在大规模时空数据集上的实验突显了后估计平滑在实际中的速度和准确性。 这些结果共同揭示了一种新的方法来考虑和整合索引变量的自然结构在机器学习中。 显示英文摘要

摘要： 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.