摘要翻译：
本文介绍了面板数据设置中回归问题的重置问题。我们首先定义了面板数据回归问题的几种变体的重置集，然后给出了构造大小多项式依赖于1/$\\varepsilon$（其中$\\varepsilon$是误差参数）和回归参数个数的重置集的有效算法--与面板数据中个体的个数或每个个体被观察的时间单位无关。我们的方法是基于Feldman-Langberg框架，其中一个关键步骤是上界“总灵敏度”，即在所有可能的回归参数选择中，所有个体时间对的最大影响之和。在经验上，我们用合成的和现实世界的数据集来评估我们的方法；用我们的方法构造的coreset的大小比完整的数据集要小得多，并且coreset确实加快了计算回归目标的运行时间。
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英文标题：
《Coresets for Regressions with Panel Data》
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作者：
Lingxiao Huang, K. Sudhir, Nisheeth K. Vishnoi
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最新提交年份：
2020
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分类信息：

一级分类：Computer Science 计算机科学
二级分类：Machine Learning 机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Computer Science 计算机科学
二级分类：Computational Geometry 计算几何
分类描述：Roughly includes material in ACM Subject Classes I.3.5 and F.2.2.
大致包括ACM课程I.3.5和F.2.2中的材料。
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一级分类：Computer Science 计算机科学
二级分类：Data Structures and Algorithms 数据结构与算法
分类描述：Covers data structures and analysis of algorithms. Roughly includes material in ACM Subject Classes E.1, E.2, F.2.1, and F.2.2.
涵盖数据结构和算法分析。大致包括ACM学科类E.1、E.2、F.2.1和F.2.2中的材料。
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一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics 统计学
二级分类：Machine Learning 机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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英文摘要：
This paper introduces the problem of coresets for regression problems to panel data settings. We first define coresets for several variants of regression problems with panel data and then present efficient algorithms to construct coresets of size that depend polynomially on 1/$\\varepsilon$ (where $\\varepsilon$ is the error parameter) and the number of regression parameters - independent of the number of individuals in the panel data or the time units each individual is observed for. Our approach is based on the Feldman-Langberg framework in which a key step is to upper bound the \"total sensitivity\" that is roughly the sum of maximum influences of all individual-time pairs taken over all possible choices of regression parameters. Empirically, we assess our approach with synthetic and real-world datasets; the coreset sizes constructed using our approach are much smaller than the full dataset and coresets indeed accelerate the running time of computing the regression objective.
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