Online Learning in Contextual Bandits using Gated Linear Networks Article Swipe
Eren Sezener
,
Marcus Hütter
,
David Budden
,
Jianan Wang
,
Joel Veness
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2002.11611
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2002.11611
We introduce a new and completely online contextual bandit algorithm called Gated Linear Contextual Bandits (GLCB). This algorithm is based on Gated Linear Networks (GLNs), a recently introduced deep learning architecture with properties well-suited to the online setting. Leveraging data-dependent gating properties of the GLN we are able to estimate prediction uncertainty with effectively zero algorithmic overhead. We empirically evaluate GLCB compared to 9 state-of-the-art algorithms that leverage deep neural networks, on a standard benchmark suite of discrete and continuous contextual bandit problems. GLCB obtains median first-place despite being the only online method, and we further support these results with a theoretical study of its convergence properties.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2002.11611
- https://arxiv.org/pdf/2002.11611
- OA Status
- green
- Cited By
- 8
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3006992011
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W3006992011Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2002.11611Digital Object Identifier
- Title
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Online Learning in Contextual Bandits using Gated Linear NetworksWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-02-21Full publication date if available
- Authors
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Eren Sezener, Marcus Hütter, David Budden, Jianan Wang, Joel VenessList of authors in order
- Landing page
-
https://arxiv.org/abs/2002.11611Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2002.11611Direct 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
- OA URL
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https://arxiv.org/pdf/2002.11611Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Online learning, Machine learning, World Wide WebTop concepts (fields/topics) attached by OpenAlex
- Cited by
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8Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1, 2021: 4, 2020: 3Per-year citation counts (last 5 years)
- References (count)
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26Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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