The Neural Testbed: Evaluating Joint Predictions Article Swipe
Ian Osband
,
Wen Zheng
,
Seyed Mohammad Asghari
,
Vikranth Dwaracherla
,
Botao Hao
,
Morteza Ibrahimi
,
Dieterich Lawson
,
Xiuyuan Lu
,
Brendan O’Donoghue
,
Benjamin Van Roy
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2110.04629
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2110.04629
Predictive distributions quantify uncertainties ignored by point estimates. This paper introduces The Neural Testbed: an open-source benchmark for controlled and principled evaluation of agents that generate such predictions. Crucially, the testbed assesses agents not only on the quality of their marginal predictions per input, but also on their joint predictions across many inputs. We evaluate a range of agents using a simple neural network data generating process. Our results indicate that some popular Bayesian deep learning agents do not fare well with joint predictions, even when they can produce accurate marginal predictions. We also show that the quality of joint predictions drives performance in downstream decision tasks. We find these results are robust across choice a wide range of generative models, and highlight the practical importance of joint predictions to the community.
Related Topics
Concepts
Testbed
Benchmark (surveying)
Computer science
Joint (building)
Range (aeronautics)
Artificial neural network
Process (computing)
Machine learning
Artificial intelligence
Quality (philosophy)
Bayesian probability
Generative grammar
Engineering
Philosophy
Geography
Geodesy
Operating system
Epistemology
Architectural engineering
Aerospace engineering
Computer network
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2110.04629
- https://arxiv.org/pdf/2110.04629
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4286907449
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4286907449Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2110.04629Digital Object Identifier
- Title
-
The Neural Testbed: Evaluating Joint PredictionsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-10-09Full publication date if available
- Authors
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Ian Osband, Wen Zheng, Seyed Mohammad Asghari, Vikranth Dwaracherla, Botao Hao, Morteza Ibrahimi, Dieterich Lawson, Xiuyuan Lu, Brendan O’Donoghue, Benjamin Van RoyList of authors in order
- Landing page
-
https://arxiv.org/abs/2110.04629Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2110.04629Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2110.04629Direct OA link when available
- Concepts
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Testbed, Benchmark (surveying), Computer science, Joint (building), Range (aeronautics), Artificial neural network, Process (computing), Machine learning, Artificial intelligence, Quality (philosophy), Bayesian probability, Generative grammar, Engineering, Philosophy, Geography, Geodesy, Operating system, Epistemology, Architectural engineering, Aerospace engineering, Computer networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2023: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Neural | 12 |
| abstract_inverted_index.across | 50, 113 |
| abstract_inverted_index.agents | 23, 32, 58, 76 |
| abstract_inverted_index.choice | 114 |
| abstract_inverted_index.drives | 101 |
| abstract_inverted_index.input, | 43 |
| abstract_inverted_index.neural | 62 |
| abstract_inverted_index.robust | 112 |
| abstract_inverted_index.simple | 61 |
| abstract_inverted_index.tasks. | 106 |
| abstract_inverted_index.ignored | 4 |
| abstract_inverted_index.inputs. | 52 |
| abstract_inverted_index.models, | 120 |
| abstract_inverted_index.network | 63 |
| abstract_inverted_index.popular | 72 |
| abstract_inverted_index.produce | 88 |
| abstract_inverted_index.quality | 37, 97 |
| abstract_inverted_index.results | 68, 110 |
| abstract_inverted_index.testbed | 30 |
| abstract_inverted_index.Bayesian | 73 |
| abstract_inverted_index.Testbed: | 13 |
| abstract_inverted_index.accurate | 89 |
| abstract_inverted_index.assesses | 31 |
| abstract_inverted_index.decision | 105 |
| abstract_inverted_index.evaluate | 54 |
| abstract_inverted_index.generate | 25 |
| abstract_inverted_index.indicate | 69 |
| abstract_inverted_index.learning | 75 |
| abstract_inverted_index.marginal | 40, 90 |
| abstract_inverted_index.process. | 66 |
| abstract_inverted_index.quantify | 2 |
| abstract_inverted_index.benchmark | 16 |
| abstract_inverted_index.highlight | 122 |
| abstract_inverted_index.practical | 124 |
| abstract_inverted_index.Crucially, | 28 |
| abstract_inverted_index.Predictive | 0 |
| abstract_inverted_index.community. | 131 |
| abstract_inverted_index.controlled | 18 |
| abstract_inverted_index.downstream | 104 |
| abstract_inverted_index.estimates. | 7 |
| abstract_inverted_index.evaluation | 21 |
| abstract_inverted_index.generating | 65 |
| abstract_inverted_index.generative | 119 |
| abstract_inverted_index.importance | 125 |
| abstract_inverted_index.introduces | 10 |
| abstract_inverted_index.principled | 20 |
| abstract_inverted_index.open-source | 15 |
| abstract_inverted_index.performance | 102 |
| abstract_inverted_index.predictions | 41, 49, 100, 128 |
| abstract_inverted_index.predictions, | 83 |
| abstract_inverted_index.predictions. | 27, 91 |
| abstract_inverted_index.distributions | 1 |
| abstract_inverted_index.uncertainties | 3 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 10 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.7400000095367432 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |