Learning to Learn by Zeroth-Order Oracle Article Swipe
Yangjun Ruan
,
Yuanhao Xiong
,
Sashank J. Reddi
,
Sanjiv Kumar
,
Cho‐Jui Hsieh
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1910.09464
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1910.09464
In the learning to learn (L2L) framework, we cast the design of optimization algorithms as a machine learning problem and use deep neural networks to learn the update rules. In this paper, we extend the L2L framework to zeroth-order (ZO) optimization setting, where no explicit gradient information is available. Our learned optimizer, modeled as recurrent neural network (RNN), first approximates gradient by ZO gradient estimator and then produces parameter update utilizing the knowledge of previous iterations. To reduce high variance effect due to ZO gradient estimator, we further introduce another RNN to learn the Gaussian sampling rule and dynamically guide the query direction sampling. Our learned optimizer outperforms hand-designed algorithms in terms of convergence rate and final solution on both synthetic and practical ZO optimization tasks (in particular, the black-box adversarial attack task, which is one of the most widely used tasks of ZO optimization). We finally conduct extensive analytical experiments to demonstrate the effectiveness of our proposed optimizer.
Related Topics
Concepts
Oracle
Computer science
Estimator
Convergence (economics)
Artificial neural network
Artificial intelligence
Gaussian
Sampling (signal processing)
Task (project management)
Gradient descent
Mathematical optimization
Algorithm
Machine learning
Mathematics
Software engineering
Economic growth
Economics
Management
Filter (signal processing)
Physics
Quantum mechanics
Statistics
Computer vision
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1910.09464
- https://arxiv.org/pdf/1910.09464
- OA Status
- green
- Cited By
- 6
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2981024147
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W2981024147Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1910.09464Digital Object Identifier
- Title
-
Learning to Learn by Zeroth-Order OracleWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-10-21Full publication date if available
- Authors
-
Yangjun Ruan, Yuanhao Xiong, Sashank J. Reddi, Sanjiv Kumar, Cho‐Jui HsiehList of authors in order
- Landing page
-
https://arxiv.org/abs/1910.09464Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/1910.09464Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1910.09464Direct OA link when available
- Concepts
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Oracle, Computer science, Estimator, Convergence (economics), Artificial neural network, Artificial intelligence, Gaussian, Sampling (signal processing), Task (project management), Gradient descent, Mathematical optimization, Algorithm, Machine learning, Mathematics, Software engineering, Economic growth, Economics, Management, Filter (signal processing), Physics, Quantum mechanics, Statistics, Computer visionTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1, 2022: 1, 2021: 2, 2020: 2Per-year citation counts (last 5 years)
- References (count)
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25Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.L2L | 35 |
| abstract_inverted_index.Our | 49, 104 |
| abstract_inverted_index.RNN | 90 |
| abstract_inverted_index.and | 19, 65, 97, 115, 121 |
| abstract_inverted_index.due | 81 |
| abstract_inverted_index.one | 135 |
| abstract_inverted_index.our | 156 |
| abstract_inverted_index.the | 1, 9, 26, 34, 71, 93, 100, 128, 137, 153 |
| abstract_inverted_index.use | 20 |
| abstract_inverted_index.(ZO) | 39 |
| abstract_inverted_index.both | 119 |
| abstract_inverted_index.cast | 8 |
| abstract_inverted_index.deep | 21 |
| abstract_inverted_index.high | 78 |
| abstract_inverted_index.most | 138 |
| abstract_inverted_index.rate | 114 |
| abstract_inverted_index.rule | 96 |
| abstract_inverted_index.then | 66 |
| abstract_inverted_index.this | 30 |
| abstract_inverted_index.used | 140 |
| abstract_inverted_index.(L2L) | 5 |
| abstract_inverted_index.final | 116 |
| abstract_inverted_index.first | 58 |
| abstract_inverted_index.guide | 99 |
| abstract_inverted_index.learn | 4, 25, 92 |
| abstract_inverted_index.query | 101 |
| abstract_inverted_index.task, | 132 |
| abstract_inverted_index.tasks | 125, 141 |
| abstract_inverted_index.terms | 111 |
| abstract_inverted_index.where | 42 |
| abstract_inverted_index.which | 133 |
| abstract_inverted_index.(RNN), | 57 |
| abstract_inverted_index.attack | 131 |
| abstract_inverted_index.design | 10 |
| abstract_inverted_index.effect | 80 |
| abstract_inverted_index.extend | 33 |
| abstract_inverted_index.neural | 22, 55 |
| abstract_inverted_index.paper, | 31 |
| abstract_inverted_index.reduce | 77 |
| abstract_inverted_index.rules. | 28 |
| abstract_inverted_index.update | 27, 69 |
| abstract_inverted_index.widely | 139 |
| abstract_inverted_index.another | 89 |
| abstract_inverted_index.conduct | 147 |
| abstract_inverted_index.finally | 146 |
| abstract_inverted_index.further | 87 |
| abstract_inverted_index.learned | 50, 105 |
| abstract_inverted_index.machine | 16 |
| abstract_inverted_index.modeled | 52 |
| abstract_inverted_index.network | 56 |
| abstract_inverted_index.problem | 18 |
| abstract_inverted_index.Gaussian | 94 |
| abstract_inverted_index.explicit | 44 |
| abstract_inverted_index.gradient | 45, 60, 63, 84 |
| abstract_inverted_index.learning | 2, 17 |
| abstract_inverted_index.networks | 23 |
| abstract_inverted_index.previous | 74 |
| abstract_inverted_index.produces | 67 |
| abstract_inverted_index.proposed | 157 |
| abstract_inverted_index.sampling | 95 |
| abstract_inverted_index.setting, | 41 |
| abstract_inverted_index.solution | 117 |
| abstract_inverted_index.variance | 79 |
| abstract_inverted_index.black-box | 129 |
| abstract_inverted_index.direction | 102 |
| abstract_inverted_index.estimator | 64 |
| abstract_inverted_index.extensive | 148 |
| abstract_inverted_index.framework | 36 |
| abstract_inverted_index.introduce | 88 |
| abstract_inverted_index.knowledge | 72 |
| abstract_inverted_index.optimizer | 106 |
| abstract_inverted_index.parameter | 68 |
| abstract_inverted_index.practical | 122 |
| abstract_inverted_index.recurrent | 54 |
| abstract_inverted_index.sampling. | 103 |
| abstract_inverted_index.synthetic | 120 |
| abstract_inverted_index.utilizing | 70 |
| abstract_inverted_index.algorithms | 13, 109 |
| abstract_inverted_index.analytical | 149 |
| abstract_inverted_index.available. | 48 |
| abstract_inverted_index.estimator, | 85 |
| abstract_inverted_index.framework, | 6 |
| abstract_inverted_index.optimizer, | 51 |
| abstract_inverted_index.optimizer. | 158 |
| abstract_inverted_index.adversarial | 130 |
| abstract_inverted_index.convergence | 113 |
| abstract_inverted_index.demonstrate | 152 |
| abstract_inverted_index.dynamically | 98 |
| abstract_inverted_index.experiments | 150 |
| abstract_inverted_index.information | 46 |
| abstract_inverted_index.iterations. | 75 |
| abstract_inverted_index.outperforms | 107 |
| abstract_inverted_index.particular, | 127 |
| abstract_inverted_index.approximates | 59 |
| abstract_inverted_index.optimization | 12, 40, 124 |
| abstract_inverted_index.zeroth-order | 38 |
| abstract_inverted_index.effectiveness | 154 |
| abstract_inverted_index.hand-designed | 108 |
| abstract_inverted_index.optimization). | 144 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |