Permutation-based Inference for Variational Learning of Directed Acyclic Graphs
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911448694784000 |
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| author | Bonilla, Edwin V. Elinas, Pantelis Zhao, He Filippone, Maurizio Kitsios, Vassili O'Kane, Terry |
| author_facet | Bonilla, Edwin V. Elinas, Pantelis Zhao, He Filippone, Maurizio Kitsios, Vassili O'Kane, Terry |
| contents | Estimating the structure of Bayesian networks as directed acyclic graphs (DAGs) from observational data is a fundamental challenge, particularly in causal discovery. Bayesian approaches excel by quantifying uncertainty and addressing identifiability, but key obstacles remain: (i) representing distributions over DAGs and (ii) estimating a posterior in the underlying combinatorial space. We introduce PIVID, a method that jointly infers a distribution over permutations and DAGs using variational inference and continuous relaxations of discrete distributions. Through experiments on synthetic and real-world datasets, we show that PIVID can outperform deterministic and Bayesian approaches, achieving superior accuracy-uncertainty trade-offs while scaling efficiently with the number of variables. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_02644 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Permutation-based Inference for Variational Learning of Directed Acyclic Graphs Bonilla, Edwin V. Elinas, Pantelis Zhao, He Filippone, Maurizio Kitsios, Vassili O'Kane, Terry Machine Learning Estimating the structure of Bayesian networks as directed acyclic graphs (DAGs) from observational data is a fundamental challenge, particularly in causal discovery. Bayesian approaches excel by quantifying uncertainty and addressing identifiability, but key obstacles remain: (i) representing distributions over DAGs and (ii) estimating a posterior in the underlying combinatorial space. We introduce PIVID, a method that jointly infers a distribution over permutations and DAGs using variational inference and continuous relaxations of discrete distributions. Through experiments on synthetic and real-world datasets, we show that PIVID can outperform deterministic and Bayesian approaches, achieving superior accuracy-uncertainty trade-offs while scaling efficiently with the number of variables. |
| title | Permutation-based Inference for Variational Learning of Directed Acyclic Graphs |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.02644 |