Artur Bekasov

Mar 27, 2024 | amazon.science | Jacek R. Golebiowski |Philipp Schmidt |Artur Bekasov |Huijun Yu

This repository contains code for evaluating the methods proposed in Learning action embeddings for off-policy evaluation. To get started, we recommend checking the Example.ipynb notebook as it clearly demonstrates benefits of the proposed method from Section 3 and implements everything in a few lines of code. To run the notebook, you only need python 3 with standard machine learning libraries.

Jan 19, 2024 | amazon.science | Jacek R. Golebiowski |Philipp Schmidt |Artur Bekasov |Matej Cief

Off-policy evaluation (OPE) methods allow us to compute the expected reward of a policy by using the logged data collected by a different policy. However, when the number of actions is large, or certain actions are under-explored by the logging policy, existing estimators based on inverse-propensity scoring (IPS) can have a high or even infinite variance.

Mar 30, 2023 | amazon.science | Tammo Rukat |Philipp Schmidt |Martin Wistuba |Artur Bekasov

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