Conservative Policy Construction Using Variational Autoencoders for Logged Data With Missing Values.
Conservative Policy Construction Using Variational Autoencoders for Logged Data With Missing Values.
复制标题
使用变分自动编码器构建具有缺失值的记录数据的保守策略。
DOI：
10.1109/tnnls.2021.3136385
复制
发表时间：
2023
影响因子：
10.4
通讯作者：
Abroshan M
中科院分区：
文献类型：
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作者：
Abroshan M
在数据驱动的决策制定的高风险应用中，如医疗保健，最重要的是学习一种政策，在存在不确定性的情况下最大化回报，同时避免潜在的危险行动。这个问题通常有两个主要的挑战。首先，由于这类应用程序的关键性质，通过在线探索学习是不可能的。因此，我们需要求助于没有反事实的观测数据集。其次，这样的数据集通常是不完美的，另外还被要素属性中缺失的值所诅咒。在本文中，我们考虑了当训练和测试数据中的特征属性中都存在缺失值时，使用记录的数据来构建个性化策略的问题。目标是当观察到有缺失值的降级版本时，建议采取行动(治疗)。我们考虑了三种应对失恋的策略。特别是，我们引入了保守策略，其中策略的设计是为了安全地处理由于未命中而产生的不确定性。为了实现这一策略，我们需要估计后验分布并使用变分自动编码器来实现这一点。具体地说，我们的方法是基于部分变分自动编码器(PVAE)的，该编码器被设计来捕获具有缺失值的特征的底层结构。
In high-stakes applications of data-driven decision-making such as healthcare, it is of paramount importance to learn a policy that maximizes the reward while avoiding potentially dangerous actions when there is uncertainty. There are two main challenges usually associated with this problem. First, learning through online exploration is not possible due to the critical nature of such applications. Therefore, we need to resort to observational datasets with no counterfactuals. Second, such datasets are usually imperfect, additionally cursed with missing values in the attributes of features. In this article, we consider the problem of constructing personalized policies using logged data when there are missing values in the attributes of features in both training and test data. The goal is to recommend an action (treatment) when, a degraded version ofwith missing values, is observed. We consider three strategies for dealing with missingness. In particular, we introduce the conservative strategy where the policy is designed to safely handle the uncertainty due to missingness. In order to implement this strategy, we need to estimate posterior distributionand use a variational autoencoder to achieve this. In particular, our method is based on partial variational autoencoders (PVAEs) that are designed to capture the underlying structure of features with missing values.