Bayesian Meta-Prior Learning Using Empirical Bayes
Bayesian Meta-Prior Learning Using Empirical Bayes
复制标题
使用经验贝叶斯的贝叶斯元先验学习
DOI：
10.1287/mnsc.2021.4136
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
G. Imbens
中科院分区：
文献类型：
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作者：
Sareh Nabi;Houssam Nassif;Joseph Hong;H. Mamani;G. Imbens
众所周知，向学习系统中添加领域知识可以改善结果。在多参数贝叶斯框架中，这些知识被纳入先验。另一方面，在现实问题中，不同的模型参数可能有不同的学习率，尤其是在数据偏斜的情况下。操作管理和管理科学应用中经常面临的两个挑战是缺乏信息先验和无法控制参数学习率。在本研究中，我们提出了一种分层经验贝叶斯方法，既解决了挑战，又可以推广到任何贝叶斯框架。我们的方法从数据本身学习经验元先验，并使用它们在广义线性模型中解耦一阶和二阶特征（或任何其他给定特征组）的学习率。因为一阶特征可能对结果有更明显的影响，所以首先专注于学习一阶权重可能会提高性能和收敛时间。我们的经验贝叶斯方法将每个组中的特征结合在一起，并使用部署模型的观察数据来经验地计算后见之明的分层先验。我们报告了我们的元先验方差估计器的无偏性、强一致性和最优频率累积后悔特性的理论结果。我们将我们的方法应用于一个标准的监督学习优化问题，以及一个在亚马逊生产系统中实现的上下文强盗设置中的在线组合优化问题。在模拟和现场实验中，我们的方法显示出明显的改进，特别是在小流量的情况下。我们的发现很有希望，因为在稀疏数据上进行优化通常是一个挑战。这篇论文被Hamid Nazerzadeh在数据驱动的规范分析专刊上接受。
Adding domain knowledge to a learning system is known to improve results. In multiparameter Bayesian frameworks, such knowledge is incorporated as a prior. On the other hand, the various model parameters can have different learning rates in real-world problems, especially with skewed data. Two often-faced challenges in operation management and management science applications are the absence of informative priors and the inability to control parameter learning rates. In this study, we propose a hierarchical empirical Bayes approach that addresses both challenges and that can generalize to any Bayesian framework. Our method learns empirical meta-priors from the data itself and uses them to decouple the learning rates of first-order and second-order features (or any other given feature grouping) in a generalized linear model. Because the first-order features are likely to have a more pronounced effect on the outcome, focusing on learning first-order weights first is likely to improve performance and convergence time. Our empirical Bayes method clamps features in each group together and uses the deployed model’s observed data to empirically compute a hierarchical prior in hindsight. We report theoretical results for the unbiasedness, strong consistency, and optimal frequentist cumulative regret properties of our meta-prior variance estimator. We apply our method to a standard supervised learning optimization problem as well as an online combinatorial optimization problem in a contextual bandit setting implemented in an Amazon production system. During both simulations and live experiments, our method shows marked improvements, especially in cases of small traffic. Our findings are promising because optimizing over sparse data is often a challenge. This paper was accepted by Hamid Nazerzadeh, special issue on data-driven prescriptive analytics.
DOI：
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发表时间：
2019-09
期刊：
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影响因子：
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作者：
A. Rajeswaran;Chelsea Finn;S. Kakade;S. Levine
通讯作者：
A. Rajeswaran;Chelsea Finn;S. Kakade;S. Levine
影响因子：
5.8
作者：
Friedman, Jerome;Hastie, Trevor;Tibshirani, Rob
通讯作者：
Tibshirani, Rob
与因果路径相关的个性化效应的估计。
DOI：
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发表时间：
2018
期刊：
Uncertainty in artificial intelligence : proceedings of the ... conference. Conference on Uncertainty in Artificial Intelligence
影响因子：
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作者：
Nabi,Razieh;Kanki,Phyllis;Shpitser,Ilya
通讯作者：
Shpitser,Ilya