POETREE: Interpretable Policy Learning with Adaptive Decision Trees
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Date
2022
Publication Type
Conference Paper
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yes
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Abstract
Building models of human decision-making from observed behaviour is critical to better understand, diagnose and support real-world policies such as clinical care. As established policy learning approaches remain focused on imitation performance, they fall short of explaining the demonstrated decision-making process. Policy Extraction through decision Trees (POETREE) is a novel framework for interpretable policy learning, compatible with fully-offline and partially-observable clinical decision environments -- and builds probabilistic tree policies determining physician actions based on patients' observations and medical history. Fully-differentiable tree architectures are grown incrementally during optimization to adapt their complexity to the modelling task, and learn a representation of patient history through recurrence, resulting in decision tree policies that adapt over time with patient information. This policy learning method outperforms the state-of-the-art on real and synthetic medical datasets, both in terms of understanding, quantifying and evaluating observed behaviour as well as in accurately replicating it -- with potential to improve future decision support systems.
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published
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The Tenth International Conference on Learning Representations (ICLR 2022)
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Pages / Article No.
Publisher
OpenReview
Event
10th International Conference on Learning Representations (ICLR 2022)
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09664 - Schölkopf, Bernhard / Schölkopf, Bernhard
09568 - Rätsch, Gunnar / Rätsch, Gunnar
02219 - ETH AI Center / ETH AI Center
Notes
Poster presented on April 26, 2022.