Cross-Validated Off-Policy Evaluation
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912162379726848 |
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| author | Cief, Matej Kveton, Branislav Kompan, Michal |
| author_facet | Cief, Matej Kveton, Branislav Kompan, Michal |
| contents | We study estimator selection and hyper-parameter tuning in off-policy evaluation. Although cross-validation is the most popular method for model selection in supervised learning, off-policy evaluation relies mostly on theory, which provides only limited guidance to practitioners. We show how to use cross-validation for off-policy evaluation. This challenges a popular belief that cross-validation in off-policy evaluation is not feasible. We evaluate our method empirically and show that it addresses a variety of use cases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_15332 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cross-Validated Off-Policy Evaluation Cief, Matej Kveton, Branislav Kompan, Michal Machine Learning We study estimator selection and hyper-parameter tuning in off-policy evaluation. Although cross-validation is the most popular method for model selection in supervised learning, off-policy evaluation relies mostly on theory, which provides only limited guidance to practitioners. We show how to use cross-validation for off-policy evaluation. This challenges a popular belief that cross-validation in off-policy evaluation is not feasible. We evaluate our method empirically and show that it addresses a variety of use cases. |
| title | Cross-Validated Off-Policy Evaluation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.15332 |