Abroshan, M;
Yip, KH;
Tekin, C;
van der Schaar, M;
(2022)
Conservative Policy Construction Using Variational Autoencoders for Logged Data With Missing Values.
IEEE Transactions on Neural Networks and Learning Systems
10.1109/TNNLS.2021.3136385.
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Abstract
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 ~X, a degraded version of Xwith 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 distribution p(X|~X) and 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.
Type: | Article |
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Title: | Conservative Policy Construction Using Variational Autoencoders for Logged Data With Missing Values |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/TNNLS.2021.3136385 |
Publisher version: | https://doi.org/10.1109/TNNLS.2021.3136385 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | Uncertainty, Estimation, Task analysis, IP networks, Noise measurement, Tuning, Training data |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10166022 |
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