Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping Article Swipe
Vikranth Dwaracherla
,
Wen Zheng
,
Ian Osband
,
Xiuyuan Lu
,
Seyed Mohammad Asghari
,
Benjamin Van Roy
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2206.03633
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2206.03633
In machine learning, an agent needs to estimate uncertainty to efficiently explore and adapt and to make effective decisions. A common approach to uncertainty estimation maintains an ensemble of models. In recent years, several approaches have been proposed for training ensembles, and conflicting views prevail with regards to the importance of various ingredients of these approaches. In this paper, we aim to address the benefits of two ingredients -- prior functions and bootstrapping -- which have come into question. We show that prior functions can significantly improve an ensemble agent's joint predictions across inputs and that bootstrapping affords additional benefits if the signal-to-noise ratio varies across inputs. Our claims are justified by both theoretical and experimental results.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2206.03633
- https://arxiv.org/pdf/2206.03633
- OA Status
- green
- Cited By
- 6
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4281876281
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4281876281Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2206.03633Digital Object Identifier
- Title
-
Ensembles for Uncertainty Estimation: Benefits of Prior Functions and BootstrappingWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-06-08Full publication date if available
- Authors
-
Vikranth Dwaracherla, Wen Zheng, Ian Osband, Xiuyuan Lu, Seyed Mohammad Asghari, Benjamin Van RoyList of authors in order
- Landing page
-
https://arxiv.org/abs/2206.03633Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2206.03633Direct 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/2206.03633Direct OA link when available
- Concepts
-
Bootstrapping (finance), Computer science, Estimation, Machine learning, Noise (video), Artificial intelligence, Uncertainty quantification, Econometrics, Mathematics, Economics, Image (mathematics), ManagementTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1, 2023: 3, 2022: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4281876281 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2206.03633 |
| ids.doi | https://doi.org/10.48550/arxiv.2206.03633 |
| ids.openalex | https://openalex.org/W4281876281 |
| fwci | |
| type | preprint |
| title | Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T12026 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.979200005531311 |
| 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 | Explainable Artificial Intelligence (XAI) |
| topics[1].id | https://openalex.org/T11512 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9726999998092651 |
| 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 | Anomaly Detection Techniques and Applications |
| topics[2].id | https://openalex.org/T11689 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.963100016117096 |
| 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 | Adversarial Robustness in Machine Learning |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C207609745 |
| concepts[0].level | 2 |
| concepts[0].score | 0.9653990268707275 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q4944086 |
| concepts[0].display_name | Bootstrapping (finance) |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.6847158670425415 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C96250715 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5903730988502502 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q965330 |
| concepts[2].display_name | Estimation |
| concepts[3].id | https://openalex.org/C119857082 |
| concepts[3].level | 1 |
| concepts[3].score | 0.5786111950874329 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[3].display_name | Machine learning |
| concepts[4].id | https://openalex.org/C99498987 |
| concepts[4].level | 3 |
| concepts[4].score | 0.47549331188201904 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q2210247 |
| concepts[4].display_name | Noise (video) |
| concepts[5].id | https://openalex.org/C154945302 |
| concepts[5].level | 1 |
| concepts[5].score | 0.4715678095817566 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[5].display_name | Artificial intelligence |
| concepts[6].id | https://openalex.org/C32230216 |
| concepts[6].level | 2 |
| concepts[6].score | 0.44103577733039856 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q7882499 |
| concepts[6].display_name | Uncertainty quantification |
| concepts[7].id | https://openalex.org/C149782125 |
| concepts[7].level | 1 |
| concepts[7].score | 0.43743717670440674 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q160039 |
| concepts[7].display_name | Econometrics |
| concepts[8].id | https://openalex.org/C33923547 |
| concepts[8].level | 0 |
| concepts[8].score | 0.16046491265296936 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[8].display_name | Mathematics |
| concepts[9].id | https://openalex.org/C162324750 |
| concepts[9].level | 0 |
| concepts[9].score | 0.10510122776031494 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q8134 |
| concepts[9].display_name | Economics |
| concepts[10].id | https://openalex.org/C115961682 |
| concepts[10].level | 2 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[10].display_name | Image (mathematics) |
| concepts[11].id | https://openalex.org/C187736073 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q2920921 |
| concepts[11].display_name | Management |
| keywords[0].id | https://openalex.org/keywords/bootstrapping |
| keywords[0].score | 0.9653990268707275 |
| keywords[0].display_name | Bootstrapping (finance) |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.6847158670425415 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/estimation |
| keywords[2].score | 0.5903730988502502 |
| keywords[2].display_name | Estimation |
| keywords[3].id | https://openalex.org/keywords/machine-learning |
| keywords[3].score | 0.5786111950874329 |
| keywords[3].display_name | Machine learning |
| keywords[4].id | https://openalex.org/keywords/noise |
| keywords[4].score | 0.47549331188201904 |
| keywords[4].display_name | Noise (video) |
| keywords[5].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[5].score | 0.4715678095817566 |
| keywords[5].display_name | Artificial intelligence |
| keywords[6].id | https://openalex.org/keywords/uncertainty-quantification |
| keywords[6].score | 0.44103577733039856 |
| keywords[6].display_name | Uncertainty quantification |
| keywords[7].id | https://openalex.org/keywords/econometrics |
| keywords[7].score | 0.43743717670440674 |
| keywords[7].display_name | Econometrics |
| keywords[8].id | https://openalex.org/keywords/mathematics |
| keywords[8].score | 0.16046491265296936 |
| keywords[8].display_name | Mathematics |
| keywords[9].id | https://openalex.org/keywords/economics |
| keywords[9].score | 0.10510122776031494 |
| keywords[9].display_name | Economics |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2206.03633 |
| 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/2206.03633 |
| 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/2206.03633 |
| locations[1].id | doi:10.48550/arxiv.2206.03633 |
| 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.2206.03633 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5085692306 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Vikranth Dwaracherla |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Dwaracherla, Vikranth |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5100665952 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-8934-9652 |
| authorships[1].author.display_name | Wen Zheng |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Wen, Zheng |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5015899120 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Ian Osband |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Osband, Ian |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5110795441 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Xiuyuan Lu |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Lu, Xiuyuan |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5083140383 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-1614-4669 |
| authorships[4].author.display_name | Seyed Mohammad Asghari |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Asghari, Seyed Mohammad |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5045543562 |
| authorships[5].author.orcid | https://orcid.org/0000-0001-8364-3746 |
| authorships[5].author.display_name | Benjamin Van Roy |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Van Roy, Benjamin |
| authorships[5].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2206.03633 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2022-06-13T00:00:00 |
| display_name | Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T12026 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.979200005531311 |
| 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 | Explainable Artificial Intelligence (XAI) |
| related_works | https://openalex.org/W1534274833, https://openalex.org/W3117246195, https://openalex.org/W156620619, https://openalex.org/W2616249226, https://openalex.org/W2098233217, https://openalex.org/W2914363205, https://openalex.org/W2997844990, https://openalex.org/W1598221548, https://openalex.org/W2081850291, https://openalex.org/W1963695443 |
| cited_by_count | 6 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 1 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 3 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2206.03633 |
| 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/2206.03633 |
| 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/2206.03633 |
| primary_location.id | pmh:oai:arXiv.org:2206.03633 |
| 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/2206.03633 |
| 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/2206.03633 |
| publication_date | 2022-06-08 |
| publication_year | 2022 |
| referenced_works_count | 0 |
| abstract_inverted_index.A | 19 |
| abstract_inverted_index.-- | 68, 73 |
| abstract_inverted_index.In | 0, 30, 56 |
| abstract_inverted_index.We | 79 |
| abstract_inverted_index.an | 3, 26, 87 |
| abstract_inverted_index.by | 111 |
| abstract_inverted_index.if | 100 |
| abstract_inverted_index.of | 28, 50, 53, 65 |
| abstract_inverted_index.to | 6, 9, 15, 22, 47, 61 |
| abstract_inverted_index.we | 59 |
| abstract_inverted_index.Our | 107 |
| abstract_inverted_index.aim | 60 |
| abstract_inverted_index.and | 12, 14, 41, 71, 94, 114 |
| abstract_inverted_index.are | 109 |
| abstract_inverted_index.can | 84 |
| abstract_inverted_index.for | 38 |
| abstract_inverted_index.the | 48, 63, 101 |
| abstract_inverted_index.two | 66 |
| abstract_inverted_index.been | 36 |
| abstract_inverted_index.both | 112 |
| abstract_inverted_index.come | 76 |
| abstract_inverted_index.have | 35, 75 |
| abstract_inverted_index.into | 77 |
| abstract_inverted_index.make | 16 |
| abstract_inverted_index.show | 80 |
| abstract_inverted_index.that | 81, 95 |
| abstract_inverted_index.this | 57 |
| abstract_inverted_index.with | 45 |
| abstract_inverted_index.adapt | 13 |
| abstract_inverted_index.agent | 4 |
| abstract_inverted_index.joint | 90 |
| abstract_inverted_index.needs | 5 |
| abstract_inverted_index.prior | 69, 82 |
| abstract_inverted_index.ratio | 103 |
| abstract_inverted_index.these | 54 |
| abstract_inverted_index.views | 43 |
| abstract_inverted_index.which | 74 |
| abstract_inverted_index.across | 92, 105 |
| abstract_inverted_index.claims | 108 |
| abstract_inverted_index.common | 20 |
| abstract_inverted_index.inputs | 93 |
| abstract_inverted_index.paper, | 58 |
| abstract_inverted_index.recent | 31 |
| abstract_inverted_index.varies | 104 |
| abstract_inverted_index.years, | 32 |
| abstract_inverted_index.address | 62 |
| abstract_inverted_index.affords | 97 |
| abstract_inverted_index.agent's | 89 |
| abstract_inverted_index.explore | 11 |
| abstract_inverted_index.improve | 86 |
| abstract_inverted_index.inputs. | 106 |
| abstract_inverted_index.machine | 1 |
| abstract_inverted_index.models. | 29 |
| abstract_inverted_index.prevail | 44 |
| abstract_inverted_index.regards | 46 |
| abstract_inverted_index.several | 33 |
| abstract_inverted_index.various | 51 |
| abstract_inverted_index.approach | 21 |
| abstract_inverted_index.benefits | 64, 99 |
| abstract_inverted_index.ensemble | 27, 88 |
| abstract_inverted_index.estimate | 7 |
| abstract_inverted_index.proposed | 37 |
| abstract_inverted_index.results. | 116 |
| abstract_inverted_index.training | 39 |
| abstract_inverted_index.effective | 17 |
| abstract_inverted_index.functions | 70, 83 |
| abstract_inverted_index.justified | 110 |
| abstract_inverted_index.learning, | 2 |
| abstract_inverted_index.maintains | 25 |
| abstract_inverted_index.question. | 78 |
| abstract_inverted_index.additional | 98 |
| abstract_inverted_index.approaches | 34 |
| abstract_inverted_index.decisions. | 18 |
| abstract_inverted_index.ensembles, | 40 |
| abstract_inverted_index.estimation | 24 |
| abstract_inverted_index.importance | 49 |
| abstract_inverted_index.approaches. | 55 |
| abstract_inverted_index.conflicting | 42 |
| abstract_inverted_index.efficiently | 10 |
| abstract_inverted_index.ingredients | 52, 67 |
| abstract_inverted_index.predictions | 91 |
| abstract_inverted_index.theoretical | 113 |
| abstract_inverted_index.uncertainty | 8, 23 |
| abstract_inverted_index.experimental | 115 |
| abstract_inverted_index.bootstrapping | 72, 96 |
| abstract_inverted_index.significantly | 85 |
| abstract_inverted_index.signal-to-noise | 102 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |