摘要翻译：
基于实时定价在电子商务平台中的应用，我们考虑了在一个环境下的收益最大化问题，在这个环境中，卖家可以利用描述客户历史和产品类型的上下文信息来预测她对产品的估价。然而，她的真实估价对卖方来说是不可观察的，只观察到交易成功-失败形式的二元结果。与通常的bandit环境不同，在我们的环境中，给定协变量的最优价格/ARM对剩余不确定性分布的详细特征很敏感。本文提出了一个残差分布为非参数的半参数模型，并给出了第一个用$\tildeo(\sqrt{n}）$refact学习回归参数和残差分布的算法。我们对算法的可伸缩实现进行了实证测试，并观察到良好的性能。
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英文标题：
《Semi-parametric dynamic contextual pricing》
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作者：
Virag Shah, Jose Blanchet, Ramesh Johari
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最新提交年份：
2019
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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：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：
  Motivated by the application of real-time pricing in e-commerce platforms, we consider the problem of revenue-maximization in a setting where the seller can leverage contextual information describing the customer's history and the product's type to predict her valuation of the product. However, her true valuation is unobservable to the seller, only binary outcome in the form of success-failure of a transaction is observed. Unlike in usual contextual bandit settings, the optimal price/arm given a covariate in our setting is sensitive to the detailed characteristics of the residual uncertainty distribution. We develop a semi-parametric model in which the residual distribution is non-parametric and provide the first algorithm which learns both regression parameters and residual distribution with $\tilde O(\sqrt{n})$ regret. We empirically test a scalable implementation of our algorithm and observe good performance.
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PDF链接：
https://arxiv.org/pdf/1901.02045