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On Discovery of Local Independence over Continuous Variables via Neural Contextual Decomposition
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hwang, Inwoo | - |
dc.contributor.author | Kwak, Yunhyeok | - |
dc.contributor.author | Song, Yeon-Ji | - |
dc.contributor.author | Zhang, Byoung-Tak | - |
dc.contributor.author | Lee, Sanghack | - |
dc.date.accessioned | 2024-05-13T05:11:02Z | - |
dc.date.available | 2024-05-13T05:11:02Z | - |
dc.date.created | 2024-04-15 | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | Proceedings of Machine Learning Research, Vol.213, pp.448-472 | - |
dc.identifier.issn | 2640-3498 | - |
dc.identifier.uri | https://hdl.handle.net/10371/201550 | - |
dc.description.abstract | Conditional independence provides a way to understand causal relationships among the variables of interest. An underlying system may exhibit more fine-grained causal relationships especially between a variable and its parents, which will be called the local independence relationships. One of the most widely studied local relationships is Context-Specific Independence (CSI), which holds in a specific assignment of conditioned variables. However, its applicability is often limited since it does not allow continuous variables: data conditioned to the specific value of a continuous variable contains few instances, if not none, making it infeasible to test independence. In this work, we define and characterize the local independence relationship that holds in a specific set of joint assignments of parental variables, which we call context-set specific independence (CSSI). We then provide a canonical representation of CSSI and prove its fundamental properties. Based on our theoretical findings, we cast the problem of discovering multiple CSSI relationships in a system as finding a partition of the joint outcome space. Finally, we propose a novel method, coined neural contextual decomposition (NCD), which learns such partition by imposing each set to induce CSSI via modeling a conditional distribution. We empirically demonstrate that the proposed method successfully discovers the ground truth local independence relationships in both synthetic dataset and complex system reflecting the real-world physical dynamics. | - |
dc.language | 영어 | - |
dc.publisher | ML Research Press | - |
dc.title | On Discovery of Local Independence over Continuous Variables via Neural Contextual Decomposition | - |
dc.type | Article | - |
dc.citation.journaltitle | Proceedings of Machine Learning Research | - |
dc.identifier.scopusid | 2-s2.0-85172911914 | - |
dc.citation.endpage | 472 | - |
dc.citation.startpage | 448 | - |
dc.citation.volume | 213 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Zhang, Byoung-Tak | - |
dc.contributor.affiliatedAuthor | Lee, Sanghack | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.subject.keywordAuthor | Causal Discovery | - |
dc.subject.keywordAuthor | Context-Specific Independence | - |
dc.subject.keywordAuthor | Local Independence | - |
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