摘要翻译：
我们考虑的设置是，分配必须反复选择，回报是未知的，但可以学习，决策受到约束。我们的模型涵盖了双边和单边匹配，即使有复杂的约束。我们提出了一种基于汤普森抽样的方法。我们的主要结果是该算法的期望遗憾上的一个先验独立的有限样本界。虽然分配的数量在参与人数中呈指数增长，但限制并不取决于这个数字。我们用美国难民重新安置的数据来说明我们算法的性能。
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英文标题：
《Adaptive Combinatorial Allocation》
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作者：
Maximilian Kasy and Alexander Teytelboym
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最新提交年份：
2020
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分类信息：

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：
We consider settings where an allocation has to be chosen repeatedly, returns are unknown but can be learned, and decisions are subject to constraints. Our model covers two-sided and one-sided matching, even with complex constraints. We propose an approach based on Thompson sampling. Our main result is a prior-independent finite-sample bound on the expected regret for this algorithm. Although the number of allocations grows exponentially in the number of participants, the bound does not depend on this number. We illustrate the performance of our algorithm using data on refugee resettlement in the United States.
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