Jacek R. Golebiowski

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 27, 2023 | arxiv.org | Jacek R. Golebiowski

arXiv:2303.15057 (cs) [Submitted on 27 Mar 2023 (v1), last revised 29 Jun 2023 (this version, v2)] Download a PDF of the paper titled Meta-Calibration Regularized Neural Networks, by Cheng Wang and Jacek Golebiowski Download PDF Submission history From: Cheng Wang [ view email] [v1] Mon, 27 Mar 2023 10:00:50 UTC (978 KB) [v2] Thu, 29 Jun 2023 12:11:34 UTC (507 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Litmaps Toggle Litmaps (What is Litmaps?) scite.ai...