Contextual Causal Bayesian Optimisation
Fuente:
arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866914301922508800 |
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| author | Arsenyan, Vahan Grosnit, Antoine Bou-Ammar, Haitham Dalalyan, Arnak |
| author_facet | Arsenyan, Vahan Grosnit, Antoine Bou-Ammar, Haitham Dalalyan, Arnak |
| contents | We introduce a unified framework for contextual and causal Bayesian optimisation, which aims to design intervention policies maximising the expectation of a target variable. Our approach leverages both observed contextual information and known causal graph structures to guide the search. Within this framework, we propose a novel algorithm that jointly optimises over policies and the sets of variables on which these policies are defined. This thereby extends and unifies two previously distinct approaches: Causal Bayesian Optimisation and Contextual Bayesian Optimisation, while also addressing their limitations in scenarios that yield suboptimal results. We derive worst-case and instance-dependent high-probability regret bounds for our algorithm. We report experimental results across diverse environments, corroborating that our approach achieves sublinear regret and reduces sample complexity in high-dimensional settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_12412 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Contextual Causal Bayesian Optimisation Arsenyan, Vahan Grosnit, Antoine Bou-Ammar, Haitham Dalalyan, Arnak Machine Learning We introduce a unified framework for contextual and causal Bayesian optimisation, which aims to design intervention policies maximising the expectation of a target variable. Our approach leverages both observed contextual information and known causal graph structures to guide the search. Within this framework, we propose a novel algorithm that jointly optimises over policies and the sets of variables on which these policies are defined. This thereby extends and unifies two previously distinct approaches: Causal Bayesian Optimisation and Contextual Bayesian Optimisation, while also addressing their limitations in scenarios that yield suboptimal results. We derive worst-case and instance-dependent high-probability regret bounds for our algorithm. We report experimental results across diverse environments, corroborating that our approach achieves sublinear regret and reduces sample complexity in high-dimensional settings. |
| title | Contextual Causal Bayesian Optimisation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2301.12412 |