Evaluating Data Influence in Meta Learning Article Swipe
Chong Ren
,
Hong Xie
,
Shuhua Yang
,
Meng Ding
,
Li Hu
,
Lei Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2501.15963
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2501.15963
As one of the most fundamental models, meta learning aims to effectively address few-shot learning challenges. However, it still faces significant issues related to the training data, such as training inefficiencies due to numerous low-contribution tasks in large datasets and substantial noise from incorrect labels. Thus, training data attribution methods are needed for meta learning. However, the dual-layer structure of mata learning complicates the modeling of training data contributions because of the interdependent influence between meta-parameters and task-specific parameters, making existing data influence evaluation tools inapplicable or inaccurate. To address these challenges, based on the influence function, we propose a general data attribution evaluation framework for meta-learning within the bilevel optimization framework. Our approach introduces task influence functions (task-IF) and instance influence functions (instance-IF) to accurately assess the impact of specific tasks and individual data points in closed forms. This framework comprehensively models data contributions across both the inner and outer training processes, capturing the direct effects of data points on meta-parameters as well as their indirect influence through task-specific parameters. We also provide several strategies to enhance computational efficiency and scalability. Experimental results demonstrate the framework's effectiveness in training data evaluation via several downstream tasks.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2501.15963
- https://arxiv.org/pdf/2501.15963
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406884816
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4406884816Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2501.15963Digital Object Identifier
- Title
-
Evaluating Data Influence in Meta LearningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-01-27Full publication date if available
- Authors
-
Chong Ren, Hong Xie, Shuhua Yang, Meng Ding, Li Hu, Lei WangList of authors in order
- Landing page
-
https://arxiv.org/abs/2501.15963Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2501.15963Direct 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/2501.15963Direct OA link when available
- Concepts
-
Meta learning (computer science), Meta-analysis, Computer science, Psychology, Economics, Medicine, Management, Internal medicine, Task (project management)Top 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/W4406884816 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2501.15963 |
| ids.doi | https://doi.org/10.48550/arxiv.2501.15963 |
| ids.openalex | https://openalex.org/W4406884816 |
| fwci | |
| type | preprint |
| title | Evaluating Data Influence in Meta Learning |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T12535 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9775999784469604 |
| 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 | Machine Learning and Data Classification |
| topics[1].id | https://openalex.org/T11122 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9526000022888184 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1706 |
| topics[1].subfield.display_name | Computer Science Applications |
| topics[1].display_name | Online Learning and Analytics |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C2781002164 |
| concepts[0].level | 3 |
| concepts[0].score | 0.5387941002845764 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q6822311 |
| concepts[0].display_name | Meta learning (computer science) |
| concepts[1].id | https://openalex.org/C95190672 |
| concepts[1].level | 2 |
| concepts[1].score | 0.46757179498672485 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q815382 |
| concepts[1].display_name | Meta-analysis |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.43275386095046997 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C15744967 |
| concepts[3].level | 0 |
| concepts[3].score | 0.33492138981819153 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q9418 |
| concepts[3].display_name | Psychology |
| concepts[4].id | https://openalex.org/C162324750 |
| concepts[4].level | 0 |
| concepts[4].score | 0.1633935272693634 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q8134 |
| concepts[4].display_name | Economics |
| concepts[5].id | https://openalex.org/C71924100 |
| concepts[5].level | 0 |
| concepts[5].score | 0.10906711220741272 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[5].display_name | Medicine |
| concepts[6].id | https://openalex.org/C187736073 |
| concepts[6].level | 1 |
| concepts[6].score | 0.059938132762908936 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2920921 |
| concepts[6].display_name | Management |
| concepts[7].id | https://openalex.org/C126322002 |
| concepts[7].level | 1 |
| concepts[7].score | 0.0 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q11180 |
| concepts[7].display_name | Internal medicine |
| concepts[8].id | https://openalex.org/C2780451532 |
| concepts[8].level | 2 |
| concepts[8].score | 0.0 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q759676 |
| concepts[8].display_name | Task (project management) |
| keywords[0].id | https://openalex.org/keywords/meta-learning |
| keywords[0].score | 0.5387941002845764 |
| keywords[0].display_name | Meta learning (computer science) |
| keywords[1].id | https://openalex.org/keywords/meta-analysis |
| keywords[1].score | 0.46757179498672485 |
| keywords[1].display_name | Meta-analysis |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.43275386095046997 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/psychology |
| keywords[3].score | 0.33492138981819153 |
| keywords[3].display_name | Psychology |
| keywords[4].id | https://openalex.org/keywords/economics |
| keywords[4].score | 0.1633935272693634 |
| keywords[4].display_name | Economics |
| keywords[5].id | https://openalex.org/keywords/medicine |
| keywords[5].score | 0.10906711220741272 |
| keywords[5].display_name | Medicine |
| keywords[6].id | https://openalex.org/keywords/management |
| keywords[6].score | 0.059938132762908936 |
| keywords[6].display_name | Management |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2501.15963 |
| 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/2501.15963 |
| 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/2501.15963 |
| locations[1].id | doi:10.48550/arxiv.2501.15963 |
| 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.2501.15963 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5110312150 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Chong Ren |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Ren, Chenyang |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5034462876 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-6610-0723 |
| authorships[1].author.display_name | Hong Xie |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Xie, Huanyi |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5100330822 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-9879-0164 |
| authorships[2].author.display_name | Shuhua Yang |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Yang, Shu |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5071989213 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-6430-3292 |
| authorships[3].author.display_name | Meng Ding |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Ding, Meng |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5011519506 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-7003-2903 |
| authorships[4].author.display_name | Li Hu |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Hu, Lijie |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5100436115 |
| authorships[5].author.orcid | https://orcid.org/0000-0003-2402-2338 |
| authorships[5].author.display_name | Lei Wang |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Wang, Di |
| 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/2501.15963 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Evaluating Data Influence in Meta Learning |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T12535 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9775999784469604 |
| 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 | Machine Learning and Data Classification |
| related_works | https://openalex.org/W4391375266, https://openalex.org/W2899084033, https://openalex.org/W2748952813, https://openalex.org/W2390279801, https://openalex.org/W4391913857, https://openalex.org/W2358668433, https://openalex.org/W4396701345, https://openalex.org/W2376932109, https://openalex.org/W2001405890, https://openalex.org/W4396696052 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2501.15963 |
| 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/2501.15963 |
| 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/2501.15963 |
| primary_location.id | pmh:oai:arXiv.org:2501.15963 |
| 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/2501.15963 |
| 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/2501.15963 |
| publication_date | 2025-01-27 |
| publication_year | 2025 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 99 |
| abstract_inverted_index.As | 0 |
| abstract_inverted_index.To | 88 |
| abstract_inverted_index.We | 171 |
| abstract_inverted_index.as | 28, 162, 164 |
| abstract_inverted_index.in | 36, 136, 188 |
| abstract_inverted_index.it | 17 |
| abstract_inverted_index.of | 2, 59, 65, 70, 129, 157 |
| abstract_inverted_index.on | 93, 160 |
| abstract_inverted_index.or | 86 |
| abstract_inverted_index.to | 10, 23, 32, 124, 176 |
| abstract_inverted_index.we | 97 |
| abstract_inverted_index.Our | 112 |
| abstract_inverted_index.and | 39, 76, 119, 132, 149, 180 |
| abstract_inverted_index.are | 50 |
| abstract_inverted_index.due | 31 |
| abstract_inverted_index.for | 52, 105 |
| abstract_inverted_index.one | 1 |
| abstract_inverted_index.the | 3, 24, 56, 63, 71, 94, 108, 127, 147, 154, 185 |
| abstract_inverted_index.via | 192 |
| abstract_inverted_index.This | 139 |
| abstract_inverted_index.aims | 9 |
| abstract_inverted_index.also | 172 |
| abstract_inverted_index.both | 146 |
| abstract_inverted_index.data | 47, 67, 81, 101, 134, 143, 158, 190 |
| abstract_inverted_index.from | 42 |
| abstract_inverted_index.mata | 60 |
| abstract_inverted_index.meta | 7, 53 |
| abstract_inverted_index.most | 4 |
| abstract_inverted_index.such | 27 |
| abstract_inverted_index.task | 115 |
| abstract_inverted_index.well | 163 |
| abstract_inverted_index.Thus, | 45 |
| abstract_inverted_index.based | 92 |
| abstract_inverted_index.data, | 26 |
| abstract_inverted_index.faces | 19 |
| abstract_inverted_index.inner | 148 |
| abstract_inverted_index.large | 37 |
| abstract_inverted_index.noise | 41 |
| abstract_inverted_index.outer | 150 |
| abstract_inverted_index.still | 18 |
| abstract_inverted_index.tasks | 35, 131 |
| abstract_inverted_index.their | 165 |
| abstract_inverted_index.these | 90 |
| abstract_inverted_index.tools | 84 |
| abstract_inverted_index.across | 145 |
| abstract_inverted_index.assess | 126 |
| abstract_inverted_index.closed | 137 |
| abstract_inverted_index.direct | 155 |
| abstract_inverted_index.forms. | 138 |
| abstract_inverted_index.impact | 128 |
| abstract_inverted_index.issues | 21 |
| abstract_inverted_index.making | 79 |
| abstract_inverted_index.models | 142 |
| abstract_inverted_index.needed | 51 |
| abstract_inverted_index.points | 135, 159 |
| abstract_inverted_index.tasks. | 195 |
| abstract_inverted_index.within | 107 |
| abstract_inverted_index.address | 12, 89 |
| abstract_inverted_index.because | 69 |
| abstract_inverted_index.between | 74 |
| abstract_inverted_index.bilevel | 109 |
| abstract_inverted_index.effects | 156 |
| abstract_inverted_index.enhance | 177 |
| abstract_inverted_index.general | 100 |
| abstract_inverted_index.labels. | 44 |
| abstract_inverted_index.methods | 49 |
| abstract_inverted_index.models, | 6 |
| abstract_inverted_index.propose | 98 |
| abstract_inverted_index.provide | 173 |
| abstract_inverted_index.related | 22 |
| abstract_inverted_index.results | 183 |
| abstract_inverted_index.several | 174, 193 |
| abstract_inverted_index.through | 168 |
| abstract_inverted_index.However, | 16, 55 |
| abstract_inverted_index.approach | 113 |
| abstract_inverted_index.datasets | 38 |
| abstract_inverted_index.existing | 80 |
| abstract_inverted_index.few-shot | 13 |
| abstract_inverted_index.indirect | 166 |
| abstract_inverted_index.instance | 120 |
| abstract_inverted_index.learning | 8, 14, 61 |
| abstract_inverted_index.modeling | 64 |
| abstract_inverted_index.numerous | 33 |
| abstract_inverted_index.specific | 130 |
| abstract_inverted_index.training | 25, 29, 46, 66, 151, 189 |
| abstract_inverted_index.(task-IF) | 118 |
| abstract_inverted_index.capturing | 153 |
| abstract_inverted_index.framework | 104, 140 |
| abstract_inverted_index.function, | 96 |
| abstract_inverted_index.functions | 117, 122 |
| abstract_inverted_index.incorrect | 43 |
| abstract_inverted_index.influence | 73, 82, 95, 116, 121, 167 |
| abstract_inverted_index.learning. | 54 |
| abstract_inverted_index.structure | 58 |
| abstract_inverted_index.accurately | 125 |
| abstract_inverted_index.downstream | 194 |
| abstract_inverted_index.dual-layer | 57 |
| abstract_inverted_index.efficiency | 179 |
| abstract_inverted_index.evaluation | 83, 103, 191 |
| abstract_inverted_index.framework. | 111 |
| abstract_inverted_index.individual | 133 |
| abstract_inverted_index.introduces | 114 |
| abstract_inverted_index.processes, | 152 |
| abstract_inverted_index.strategies | 175 |
| abstract_inverted_index.attribution | 48, 102 |
| abstract_inverted_index.challenges, | 91 |
| abstract_inverted_index.challenges. | 15 |
| abstract_inverted_index.complicates | 62 |
| abstract_inverted_index.demonstrate | 184 |
| abstract_inverted_index.effectively | 11 |
| abstract_inverted_index.framework's | 186 |
| abstract_inverted_index.fundamental | 5 |
| abstract_inverted_index.inaccurate. | 87 |
| abstract_inverted_index.parameters, | 78 |
| abstract_inverted_index.parameters. | 170 |
| abstract_inverted_index.significant | 20 |
| abstract_inverted_index.substantial | 40 |
| abstract_inverted_index.Experimental | 182 |
| abstract_inverted_index.inapplicable | 85 |
| abstract_inverted_index.optimization | 110 |
| abstract_inverted_index.scalability. | 181 |
| abstract_inverted_index.(instance-IF) | 123 |
| abstract_inverted_index.computational | 178 |
| abstract_inverted_index.contributions | 68, 144 |
| abstract_inverted_index.effectiveness | 187 |
| abstract_inverted_index.meta-learning | 106 |
| abstract_inverted_index.task-specific | 77, 169 |
| abstract_inverted_index.inefficiencies | 30 |
| abstract_inverted_index.interdependent | 72 |
| abstract_inverted_index.comprehensively | 141 |
| abstract_inverted_index.meta-parameters | 75, 161 |
| abstract_inverted_index.low-contribution | 34 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |