Differentiable Bayesian Structure Learning with Acyclicity Assurance Article Swipe
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2309.01392
Score-based approaches in the structure learning task are thriving because of their scalability. Continuous relaxation has been the key reason for this advancement. Despite achieving promising outcomes, most of these methods are still struggling to ensure that the graphs generated from the latent space are acyclic by minimizing a defined score. There has also been another trend of permutation-based approaches, which concern the search for the topological ordering of the variables in the directed acyclic graph in order to limit the search space of the graph. In this study, we propose an alternative approach for strictly constraining the acyclicty of the graphs with an integration of the knowledge from the topological orderings. Our approach can reduce inference complexity while ensuring the structures of the generated graphs to be acyclic. Our empirical experiments with simulated and real-world data show that our approach can outperform related Bayesian score-based approaches.
Related Topics
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2309.01392
- https://arxiv.org/pdf/2309.01392
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386555620
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4386555620Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2309.01392Digital Object Identifier
- Title
-
Differentiable Bayesian Structure Learning with Acyclicity AssuranceWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-09-04Full publication date if available
- Authors
-
Quang-Duy Tran, Phuoc Nguyen, Bao Duong, Thin NguyenList of authors in order
- Landing page
-
https://arxiv.org/abs/2309.01392Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2309.01392Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2309.01392Direct OA link when available
- Concepts
-
Directed acyclic graph, Computer science, Scalability, Bayesian network, Inference, Theoretical computer science, Differentiable function, Permutation (music), Machine learning, Artificial intelligence, Algorithm, Mathematics, Physics, Acoustics, Database, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4386555620 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2309.01392 |
| ids.doi | https://doi.org/10.48550/arxiv.2309.01392 |
| ids.openalex | https://openalex.org/W4386555620 |
| fwci | |
| type | preprint |
| title | Differentiable Bayesian Structure Learning with Acyclicity Assurance |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11303 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9879000186920166 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Bayesian Modeling and Causal Inference |
| topics[1].id | https://openalex.org/T12535 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9746000170707703 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Machine Learning and Data Classification |
| topics[2].id | https://openalex.org/T11307 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.942799985408783 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | Domain Adaptation and Few-Shot Learning |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C74197172 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8552409410476685 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1195339 |
| concepts[0].display_name | Directed acyclic graph |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.6278259754180908 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C48044578 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6241856813430786 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q727490 |
| concepts[2].display_name | Scalability |
| concepts[3].id | https://openalex.org/C33724603 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5785565376281738 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q812540 |
| concepts[3].display_name | Bayesian network |
| concepts[4].id | https://openalex.org/C2776214188 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5076321363449097 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q408386 |
| concepts[4].display_name | Inference |
| concepts[5].id | https://openalex.org/C80444323 |
| concepts[5].level | 1 |
| concepts[5].score | 0.4803653657436371 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q2878974 |
| concepts[5].display_name | Theoretical computer science |
| concepts[6].id | https://openalex.org/C202615002 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4231227934360504 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q783507 |
| concepts[6].display_name | Differentiable function |
| concepts[7].id | https://openalex.org/C21308566 |
| concepts[7].level | 2 |
| concepts[7].score | 0.411077618598938 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q7169365 |
| concepts[7].display_name | Permutation (music) |
| concepts[8].id | https://openalex.org/C119857082 |
| concepts[8].level | 1 |
| concepts[8].score | 0.4027276635169983 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[8].display_name | Machine learning |
| concepts[9].id | https://openalex.org/C154945302 |
| concepts[9].level | 1 |
| concepts[9].score | 0.36443910002708435 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[9].display_name | Artificial intelligence |
| concepts[10].id | https://openalex.org/C11413529 |
| concepts[10].level | 1 |
| concepts[10].score | 0.2223871350288391 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[10].display_name | Algorithm |
| concepts[11].id | https://openalex.org/C33923547 |
| concepts[11].level | 0 |
| concepts[11].score | 0.21759313344955444 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[11].display_name | Mathematics |
| concepts[12].id | https://openalex.org/C121332964 |
| concepts[12].level | 0 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[12].display_name | Physics |
| concepts[13].id | https://openalex.org/C24890656 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q82811 |
| concepts[13].display_name | Acoustics |
| concepts[14].id | https://openalex.org/C77088390 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q8513 |
| concepts[14].display_name | Database |
| concepts[15].id | https://openalex.org/C134306372 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[15].display_name | Mathematical analysis |
| keywords[0].id | https://openalex.org/keywords/directed-acyclic-graph |
| keywords[0].score | 0.8552409410476685 |
| keywords[0].display_name | Directed acyclic graph |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.6278259754180908 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/scalability |
| keywords[2].score | 0.6241856813430786 |
| keywords[2].display_name | Scalability |
| keywords[3].id | https://openalex.org/keywords/bayesian-network |
| keywords[3].score | 0.5785565376281738 |
| keywords[3].display_name | Bayesian network |
| keywords[4].id | https://openalex.org/keywords/inference |
| keywords[4].score | 0.5076321363449097 |
| keywords[4].display_name | Inference |
| keywords[5].id | https://openalex.org/keywords/theoretical-computer-science |
| keywords[5].score | 0.4803653657436371 |
| keywords[5].display_name | Theoretical computer science |
| keywords[6].id | https://openalex.org/keywords/differentiable-function |
| keywords[6].score | 0.4231227934360504 |
| keywords[6].display_name | Differentiable function |
| keywords[7].id | https://openalex.org/keywords/permutation |
| keywords[7].score | 0.411077618598938 |
| keywords[7].display_name | Permutation (music) |
| keywords[8].id | https://openalex.org/keywords/machine-learning |
| keywords[8].score | 0.4027276635169983 |
| keywords[8].display_name | Machine learning |
| keywords[9].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[9].score | 0.36443910002708435 |
| keywords[9].display_name | Artificial intelligence |
| keywords[10].id | https://openalex.org/keywords/algorithm |
| keywords[10].score | 0.2223871350288391 |
| keywords[10].display_name | Algorithm |
| keywords[11].id | https://openalex.org/keywords/mathematics |
| keywords[11].score | 0.21759313344955444 |
| keywords[11].display_name | Mathematics |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2309.01392 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2309.01392 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2309.01392 |
| locations[1].id | doi:10.48550/arxiv.2309.01392 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2309.01392 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5103228838 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-2244-9154 |
| authorships[0].author.display_name | Quang-Duy Tran |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Tran, Quang-Duy |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5101580890 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-1649-2519 |
| authorships[1].author.display_name | Phuoc Nguyen |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Nguyen, Phuoc |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5102811209 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-9850-0270 |
| authorships[2].author.display_name | Bao Duong |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Duong, Bao |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5100705489 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-3467-8963 |
| authorships[3].author.display_name | Thin Nguyen |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Nguyen, Thin |
| authorships[3].is_corresponding | False |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2309.01392 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Differentiable Bayesian Structure Learning with Acyclicity Assurance |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T11303 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9879000186920166 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Bayesian Modeling and Causal Inference |
| related_works | https://openalex.org/W4285277090, https://openalex.org/W4327738859, https://openalex.org/W4302345037, https://openalex.org/W2348722996, https://openalex.org/W2731094954, https://openalex.org/W1505105018, https://openalex.org/W1750699579, https://openalex.org/W12712126, https://openalex.org/W1492817421, https://openalex.org/W1511983606 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2309.01392 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2309.01392 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2309.01392 |
| primary_location.id | pmh:oai:arXiv.org:2309.01392 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2309.01392 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2309.01392 |
| publication_date | 2023-09-04 |
| publication_year | 2023 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 48 |
| abstract_inverted_index.In | 86 |
| abstract_inverted_index.an | 91, 103 |
| abstract_inverted_index.be | 127 |
| abstract_inverted_index.by | 46 |
| abstract_inverted_index.in | 2, 71, 76 |
| abstract_inverted_index.of | 10, 28, 57, 68, 83, 99, 105, 122 |
| abstract_inverted_index.to | 34, 78, 126 |
| abstract_inverted_index.we | 89 |
| abstract_inverted_index.Our | 112, 129 |
| abstract_inverted_index.and | 134 |
| abstract_inverted_index.are | 7, 31, 44 |
| abstract_inverted_index.can | 114, 141 |
| abstract_inverted_index.for | 20, 64, 94 |
| abstract_inverted_index.has | 15, 52 |
| abstract_inverted_index.key | 18 |
| abstract_inverted_index.our | 139 |
| abstract_inverted_index.the | 3, 17, 37, 41, 62, 65, 69, 72, 80, 84, 97, 100, 106, 109, 120, 123 |
| abstract_inverted_index.also | 53 |
| abstract_inverted_index.been | 16, 54 |
| abstract_inverted_index.data | 136 |
| abstract_inverted_index.from | 40, 108 |
| abstract_inverted_index.most | 27 |
| abstract_inverted_index.show | 137 |
| abstract_inverted_index.task | 6 |
| abstract_inverted_index.that | 36, 138 |
| abstract_inverted_index.this | 21, 87 |
| abstract_inverted_index.with | 102, 132 |
| abstract_inverted_index.There | 51 |
| abstract_inverted_index.graph | 75 |
| abstract_inverted_index.limit | 79 |
| abstract_inverted_index.order | 77 |
| abstract_inverted_index.space | 43, 82 |
| abstract_inverted_index.still | 32 |
| abstract_inverted_index.their | 11 |
| abstract_inverted_index.these | 29 |
| abstract_inverted_index.trend | 56 |
| abstract_inverted_index.which | 60 |
| abstract_inverted_index.while | 118 |
| abstract_inverted_index.ensure | 35 |
| abstract_inverted_index.graph. | 85 |
| abstract_inverted_index.graphs | 38, 101, 125 |
| abstract_inverted_index.latent | 42 |
| abstract_inverted_index.reason | 19 |
| abstract_inverted_index.reduce | 115 |
| abstract_inverted_index.score. | 50 |
| abstract_inverted_index.search | 63, 81 |
| abstract_inverted_index.study, | 88 |
| abstract_inverted_index.Despite | 23 |
| abstract_inverted_index.acyclic | 45, 74 |
| abstract_inverted_index.another | 55 |
| abstract_inverted_index.because | 9 |
| abstract_inverted_index.concern | 61 |
| abstract_inverted_index.defined | 49 |
| abstract_inverted_index.methods | 30 |
| abstract_inverted_index.propose | 90 |
| abstract_inverted_index.related | 143 |
| abstract_inverted_index.Bayesian | 144 |
| abstract_inverted_index.acyclic. | 128 |
| abstract_inverted_index.approach | 93, 113, 140 |
| abstract_inverted_index.directed | 73 |
| abstract_inverted_index.ensuring | 119 |
| abstract_inverted_index.learning | 5 |
| abstract_inverted_index.ordering | 67 |
| abstract_inverted_index.strictly | 95 |
| abstract_inverted_index.thriving | 8 |
| abstract_inverted_index.achieving | 24 |
| abstract_inverted_index.acyclicty | 98 |
| abstract_inverted_index.empirical | 130 |
| abstract_inverted_index.generated | 39, 124 |
| abstract_inverted_index.inference | 116 |
| abstract_inverted_index.knowledge | 107 |
| abstract_inverted_index.outcomes, | 26 |
| abstract_inverted_index.promising | 25 |
| abstract_inverted_index.simulated | 133 |
| abstract_inverted_index.structure | 4 |
| abstract_inverted_index.variables | 70 |
| abstract_inverted_index.Continuous | 13 |
| abstract_inverted_index.approaches | 1 |
| abstract_inverted_index.complexity | 117 |
| abstract_inverted_index.minimizing | 47 |
| abstract_inverted_index.orderings. | 111 |
| abstract_inverted_index.outperform | 142 |
| abstract_inverted_index.real-world | 135 |
| abstract_inverted_index.relaxation | 14 |
| abstract_inverted_index.structures | 121 |
| abstract_inverted_index.struggling | 33 |
| abstract_inverted_index.Score-based | 0 |
| abstract_inverted_index.alternative | 92 |
| abstract_inverted_index.approaches, | 59 |
| abstract_inverted_index.approaches. | 146 |
| abstract_inverted_index.experiments | 131 |
| abstract_inverted_index.integration | 104 |
| abstract_inverted_index.score-based | 145 |
| abstract_inverted_index.topological | 66, 110 |
| abstract_inverted_index.advancement. | 22 |
| abstract_inverted_index.constraining | 96 |
| abstract_inverted_index.scalability. | 12 |
| abstract_inverted_index.permutation-based | 58 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |