Exponentiated Gradient Reweighting for Robust Training Under Label Noise and Beyond Article Swipe
Negin Majidi
,
Ehsan Amid
,
Hossein Talebi
,
Manfred K. Warmuth
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2104.01493
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2104.01493
Many learning tasks in machine learning can be viewed as taking a gradient step towards minimizing the average loss of a batch of examples in each training iteration. When noise is prevalent in the data, this uniform treatment of examples can lead to overfitting to noisy examples with larger loss values and result in poor generalization. Inspired by the expert setting in on-line learning, we present a flexible approach to learning from noisy examples. Specifically, we treat each training example as an expert and maintain a distribution over all examples. We alternate between updating the parameters of the model using gradient descent and updating the example weights using the exponentiated gradient update. Unlike other related methods, our approach handles a general class of loss functions and can be applied to a wide range of noise types and applications. We show the efficacy of our approach for multiple learning settings, namely noisy principal component analysis and a variety of noisy classification problems.
Related Topics
Concepts
Overfitting
Computer science
Noise (video)
Generalization
Artificial intelligence
Gradient descent
Range (aeronautics)
Machine learning
Variety (cybernetics)
Pattern recognition (psychology)
Artificial neural network
Mathematics
Mathematical analysis
Image (mathematics)
Composite material
Materials science
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2104.01493
- https://arxiv.org/pdf/2104.01493
- OA Status
- green
- Cited By
- 3
- References
- 58
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3147674054
All OpenAlex metadata
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https://openalex.org/W3147674054Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2104.01493Digital Object Identifier
- Title
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Exponentiated Gradient Reweighting for Robust Training Under Label Noise and BeyondWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
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2021Year of publication
- Publication date
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2021-04-03Full publication date if available
- Authors
-
Negin Majidi, Ehsan Amid, Hossein Talebi, Manfred K. WarmuthList of authors in order
- Landing page
-
https://arxiv.org/abs/2104.01493Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2104.01493Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2104.01493Direct OA link when available
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Overfitting, Computer science, Noise (video), Generalization, Artificial intelligence, Gradient descent, Range (aeronautics), Machine learning, Variety (cybernetics), Pattern recognition (psychology), Artificial neural network, Mathematics, Mathematical analysis, Image (mathematics), Composite material, Materials scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1, 2021: 1, 2020: 1Per-year citation counts (last 5 years)
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58Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.larger | 48 |
| abstract_inverted_index.namely | 149 |
| abstract_inverted_index.result | 52 |
| abstract_inverted_index.taking | 10 |
| abstract_inverted_index.values | 50 |
| abstract_inverted_index.viewed | 8 |
| abstract_inverted_index.applied | 128 |
| abstract_inverted_index.average | 17 |
| abstract_inverted_index.between | 92 |
| abstract_inverted_index.descent | 101 |
| abstract_inverted_index.example | 79, 105 |
| abstract_inverted_index.general | 120 |
| abstract_inverted_index.handles | 118 |
| abstract_inverted_index.machine | 4 |
| abstract_inverted_index.on-line | 62 |
| abstract_inverted_index.present | 65 |
| abstract_inverted_index.related | 114 |
| abstract_inverted_index.setting | 60 |
| abstract_inverted_index.towards | 14 |
| abstract_inverted_index.uniform | 36 |
| abstract_inverted_index.update. | 111 |
| abstract_inverted_index.variety | 156 |
| abstract_inverted_index.weights | 106 |
| abstract_inverted_index.Inspired | 56 |
| abstract_inverted_index.analysis | 153 |
| abstract_inverted_index.approach | 68, 117, 144 |
| abstract_inverted_index.efficacy | 141 |
| abstract_inverted_index.examples | 23, 39, 46 |
| abstract_inverted_index.flexible | 67 |
| abstract_inverted_index.gradient | 12, 100, 110 |
| abstract_inverted_index.learning | 1, 5, 70, 147 |
| abstract_inverted_index.maintain | 84 |
| abstract_inverted_index.methods, | 115 |
| abstract_inverted_index.multiple | 146 |
| abstract_inverted_index.training | 26, 78 |
| abstract_inverted_index.updating | 93, 103 |
| abstract_inverted_index.alternate | 91 |
| abstract_inverted_index.component | 152 |
| abstract_inverted_index.examples. | 73, 89 |
| abstract_inverted_index.functions | 124 |
| abstract_inverted_index.learning, | 63 |
| abstract_inverted_index.prevalent | 31 |
| abstract_inverted_index.principal | 151 |
| abstract_inverted_index.problems. | 160 |
| abstract_inverted_index.settings, | 148 |
| abstract_inverted_index.treatment | 37 |
| abstract_inverted_index.iteration. | 27 |
| abstract_inverted_index.minimizing | 15 |
| abstract_inverted_index.parameters | 95 |
| abstract_inverted_index.overfitting | 43 |
| abstract_inverted_index.distribution | 86 |
| abstract_inverted_index.Specifically, | 74 |
| abstract_inverted_index.applications. | 137 |
| abstract_inverted_index.exponentiated | 109 |
| abstract_inverted_index.classification | 159 |
| abstract_inverted_index.generalization. | 55 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/1 |
| sustainable_development_goals[0].score | 0.699999988079071 |
| sustainable_development_goals[0].display_name | No poverty |
| citation_normalized_percentile |