Hypermodels for Exploration Article Swipe
Vikranth Dwaracherla
,
Xiuyuan Lu
,
Morteza Ibrahimi
,
Ian Osband
,
Zheng Wen
,
Benjamin Van Roy
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2006.07464
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2006.07464
We study the use of hypermodels to represent epistemic uncertainty and guide exploration. This generalizes and extends the use of ensembles to approximate Thompson sampling. The computational cost of training an ensemble grows with its size, and as such, prior work has typically been limited to ensembles with tens of elements. We show that alternative hypermodels can enjoy dramatic efficiency gains, enabling behavior that would otherwise require hundreds or thousands of elements, and even succeed in situations where ensemble methods fail to learn regardless of size. This allows more accurate approximation of Thompson sampling as well as use of more sophisticated exploration schemes. In particular, we consider an approximate form of information-directed sampling and demonstrate performance gains relative to Thompson sampling. As alternatives to ensembles, we consider linear and neural network hypermodels, also known as hypernetworks. We prove that, with neural network base models, a linear hypermodel can represent essentially any distribution over functions, and as such, hypernetworks are no more expressive.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- http://arxiv.org/abs/2006.07464
- https://arxiv.org/pdf/2006.07464
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- green
- Cited By
- 10
- References
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W2996558820Canonical identifier for this work in OpenAlex
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https://doi.org/10.48550/arxiv.2006.07464Digital Object Identifier
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Hypermodels for ExplorationWork title
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articleOpenAlex work type
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enPrimary language
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2020Year of publication
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2020-06-12Full publication date if available
- Authors
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Vikranth Dwaracherla, Xiuyuan Lu, Morteza Ibrahimi, Ian Osband, Zheng Wen, Benjamin Van RoyList of authors in order
- Landing page
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https://arxiv.org/abs/2006.07464Publisher landing page
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https://arxiv.org/pdf/2006.07464Direct link to full text PDF
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greenOpen access status per OpenAlex
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Computer science, Sampling (signal processing), Artificial neural network, Machine learning, Artificial intelligence, Theoretical computer science, Filter (signal processing), Computer visionTop concepts (fields/topics) attached by OpenAlex
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10Total citation count in OpenAlex
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2021: 6, 2020: 4Per-year citation counts (last 5 years)
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.also | 132 |
| abstract_inverted_index.base | 142 |
| abstract_inverted_index.been | 43 |
| abstract_inverted_index.cost | 27 |
| abstract_inverted_index.even | 73 |
| abstract_inverted_index.fail | 80 |
| abstract_inverted_index.form | 109 |
| abstract_inverted_index.more | 88, 99, 160 |
| abstract_inverted_index.over | 152 |
| abstract_inverted_index.show | 52 |
| abstract_inverted_index.tens | 48 |
| abstract_inverted_index.that | 53, 63 |
| abstract_inverted_index.well | 95 |
| abstract_inverted_index.with | 33, 47, 139 |
| abstract_inverted_index.work | 40 |
| abstract_inverted_index.enjoy | 57 |
| abstract_inverted_index.gains | 116 |
| abstract_inverted_index.grows | 32 |
| abstract_inverted_index.guide | 11 |
| abstract_inverted_index.known | 133 |
| abstract_inverted_index.learn | 82 |
| abstract_inverted_index.prior | 39 |
| abstract_inverted_index.prove | 137 |
| abstract_inverted_index.size, | 35 |
| abstract_inverted_index.size. | 85 |
| abstract_inverted_index.study | 1 |
| abstract_inverted_index.such, | 38, 156 |
| abstract_inverted_index.that, | 138 |
| abstract_inverted_index.where | 77 |
| abstract_inverted_index.would | 64 |
| abstract_inverted_index.allows | 87 |
| abstract_inverted_index.gains, | 60 |
| abstract_inverted_index.linear | 127, 145 |
| abstract_inverted_index.neural | 129, 140 |
| abstract_inverted_index.extends | 16 |
| abstract_inverted_index.limited | 44 |
| abstract_inverted_index.methods | 79 |
| abstract_inverted_index.models, | 143 |
| abstract_inverted_index.network | 130, 141 |
| abstract_inverted_index.require | 66 |
| abstract_inverted_index.succeed | 74 |
| abstract_inverted_index.Thompson | 23, 92, 119 |
| abstract_inverted_index.accurate | 89 |
| abstract_inverted_index.behavior | 62 |
| abstract_inverted_index.consider | 106, 126 |
| abstract_inverted_index.dramatic | 58 |
| abstract_inverted_index.enabling | 61 |
| abstract_inverted_index.ensemble | 31, 78 |
| abstract_inverted_index.hundreds | 67 |
| abstract_inverted_index.relative | 117 |
| abstract_inverted_index.sampling | 93, 112 |
| abstract_inverted_index.schemes. | 102 |
| abstract_inverted_index.training | 29 |
| abstract_inverted_index.elements, | 71 |
| abstract_inverted_index.elements. | 50 |
| abstract_inverted_index.ensembles | 20, 46 |
| abstract_inverted_index.epistemic | 8 |
| abstract_inverted_index.otherwise | 65 |
| abstract_inverted_index.represent | 7, 148 |
| abstract_inverted_index.sampling. | 24, 120 |
| abstract_inverted_index.thousands | 69 |
| abstract_inverted_index.typically | 42 |
| abstract_inverted_index.efficiency | 59 |
| abstract_inverted_index.ensembles, | 124 |
| abstract_inverted_index.functions, | 153 |
| abstract_inverted_index.hypermodel | 146 |
| abstract_inverted_index.regardless | 83 |
| abstract_inverted_index.situations | 76 |
| abstract_inverted_index.alternative | 54 |
| abstract_inverted_index.approximate | 22, 108 |
| abstract_inverted_index.demonstrate | 114 |
| abstract_inverted_index.essentially | 149 |
| abstract_inverted_index.exploration | 101 |
| abstract_inverted_index.expressive. | 161 |
| abstract_inverted_index.generalizes | 14 |
| abstract_inverted_index.hypermodels | 5, 55 |
| abstract_inverted_index.particular, | 104 |
| abstract_inverted_index.performance | 115 |
| abstract_inverted_index.uncertainty | 9 |
| abstract_inverted_index.alternatives | 122 |
| abstract_inverted_index.distribution | 151 |
| abstract_inverted_index.exploration. | 12 |
| abstract_inverted_index.hypermodels, | 131 |
| abstract_inverted_index.approximation | 90 |
| abstract_inverted_index.computational | 26 |
| abstract_inverted_index.hypernetworks | 157 |
| abstract_inverted_index.sophisticated | 100 |
| abstract_inverted_index.hypernetworks. | 135 |
| abstract_inverted_index.information-directed | 111 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |