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Merge pull request #173 from st-tech/update-version
Update version to 0.5.5
2 parents 9d62615 + 721a3f3 commit 456a1ea

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README.md

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# Project Team
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- [Yuta Saito](https://usaito.github.io/) (**Main Contributor**; Cornell University)
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- [Yuta Saito](https://usait0.com/en/) (**Main Contributor**; Cornell University)
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- [Shunsuke Aihara](https://www.linkedin.com/in/shunsukeaihara/) (ZOZO Research)
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- Megumi Matsutani (ZOZO Research)
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- [Yusuke Narita](https://www.yusuke-narita.com/) (Hanjuku-kaso Co., Ltd. / Yale University)
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19. James McInerney, Brian Brost, Praveen Chandar, Rishabh Mehrotra, and Benjamin Carterette. [Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions](https://arxiv.org/abs/2007.12986). In *Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery&Data Mining*, 1779–1788, 2020.
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20. Yusuke Narita, Shota Yasui, and Kohei Yata. [Debiased Off-Policy Evaluation for Recommendation Systems](https://arxiv.org/abs/2002.08536). *arXiv preprint arXiv:2002.08536*, 2020.
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20. Yusuke Narita, Shota Yasui, and Kohei Yata. [Debiased Off-Policy Evaluation for Recommendation Systems](https://dl.acm.org/doi/10.1145/3460231.3474231). In *Proceedings of the Fifteenth ACM Conference on Recommender Systems*, 372-379, 2021.
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21. Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://arxiv.org/abs/2005.00687). In *Advances in Neural Information Processing Systems*. 2020.
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22. Noveen Sachdeva, Yi Su, and Thorsten Joachims. [Off-policy Bandits with Deficient Support](https://dl.acm.org/doi/10.1145/3394486.3403139). In *Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining*, 965-975, 2021.
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23. Yi Su, Pavithra Srinath, and Akshay Krishnamurthy. [Adaptive Estimator Selection for Off-Policy Evaluation](https://proceedings.mlr.press/v119/su20d.html). In *Proceedings of the 38th International Conference on Machine Learning*, 9196-9205, 2021.
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24. Haruka Kiyohara, Yuta Saito, Tatsuya Matsuhiro, Yusuke Narita, Nobuyuki Shimizu, Yasuo Yamamoto. [Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model](https://dl.acm.org/doi/10.1145/3488560.3498380). In *Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining*, 487-497, 2022.
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25. Yuta Saito and Thorsten Joachims. [Off-Policy Evaluation for Large Action Spaces via Embeddings](https://arxiv.org/abs/2202.06317). In *Proceedings of the 39th International Conference on Machine Learning*, 2022.
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</details>

README_JN.md

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# プロジェクトチーム
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- [齋藤優太](https://usaito.github.io/) (**Main Contributor**; 半熟仮想株式会社 / コーネル大学)
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- [齋藤優太](https://usait0.com/ja/) (**Main Contributor**; 半熟仮想株式会社 / コーネル大学)
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- [粟飯原俊介](https://www.linkedin.com/in/shunsukeaihara/) (ZOZO研究所)
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- 松谷恵 (ZOZO研究所)
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- [成田悠輔](https://www.yusuke-narita.com/) (半熟仮想株式会社 / イェール大学)
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19. James McInerney, Brian Brost, Praveen Chandar, Rishabh Mehrotra, and Benjamin Carterette. [Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions](https://arxiv.org/abs/2007.12986). In *Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery&Data Mining*, 1779–1788, 2020.
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20. Yusuke Narita, Shota Yasui, and Kohei Yata. [Debiased Off-Policy Evaluation for Recommendation Systems](https://arxiv.org/abs/2002.08536). *arXiv preprint arXiv:2002.08536*, 2020.
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20. Yusuke Narita, Shota Yasui, and Kohei Yata. [Debiased Off-Policy Evaluation for Recommendation Systems](https://dl.acm.org/doi/10.1145/3460231.3474231). In *Proceedings of the Fifteenth ACM Conference on Recommender Systems*, 372-379, 2021.
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21. Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://arxiv.org/abs/2005.00687). In *Advances in Neural Information Processing Systems*. 2020.
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22. Noveen Sachdeva, Yi Su, and Thorsten Joachims. [Off-policy Bandits with Deficient Support](https://dl.acm.org/doi/10.1145/3394486.3403139). In *Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining*, 965-975, 2021.
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23. Yi Su, Pavithra Srinath, and Akshay Krishnamurthy. [Adaptive Estimator Selection for Off-Policy Evaluation](https://proceedings.mlr.press/v119/su20d.html). In *Proceedings of the 38th International Conference on Machine Learning*, 9196-9205, 2021.
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24. Haruka Kiyohara, Yuta Saito, Tatsuya Matsuhiro, Yusuke Narita, Nobuyuki Shimizu, Yasuo Yamamoto. [Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model](https://dl.acm.org/doi/10.1145/3488560.3498380). In *Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining*, 487-497, 2022.
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25. Yuta Saito and Thorsten Joachims. [Off-Policy Evaluation for Large Action Spaces via Embeddings](https://arxiv.org/abs/2202.06317). In *Proceedings of the 39th International Conference on Machine Learning*, 2022.
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</details>
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<details>

obp/version.py

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__version__ = "0.5.4"
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__version__ = "0.5.5"

poetry.lock

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pyproject.toml

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[tool.poetry]
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name = "obp"
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version = "0.5.4"
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version = "0.5.5"
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description = "Open Bandit Pipeline: a python library for off-policy evaluation and learning"
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authors = ["Yuta Saito <ys552@cornell.edu>"]
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license = "Apache License 2.0"
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pyieoe = "^0.1.1"
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pingouin = "^0.4.0"
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mypy-extensions = "^0.4.3"
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Pillow = "9.1.0"
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Pillow = "9.1.1"
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[tool.poetry.dev-dependencies]
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flake8 = "^3.9.2"

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