Deep Bayesian Active Learning for Accelerating Stochastic Simulation Article Swipe
Dongxia Wu
,
Ruijia Niu
,
Matteo Chinazzi
,
Alessandro Vespignani
,
Yi-An Ma
,
Rose Yu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1145/3580305.3599300
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1145/3580305.3599300
Stochastic simulations such as large-scale, spatiotemporal, age-structured epidemic models are computationally expensive at fine-grained resolution. While deep surrogate models can speed up the simulations, doing so for stochastic simulations and with active learning approaches is an underexplored area. We propose Interactive Neural Process (INP), a deep Bayesian active learning framework for learning deep surrogate models to accelerate stochastic simulations. INP consists of two components, a spatiotemporal surrogate model built upon Neural Process (NP) family and an acquisition function for active learning. For surrogate modeling, we develop Spatiotemporal Neural Process (STNP) to mimic the simulator dynamics. For active learning, we propose a novel acquisition function, Latent Information Gain (LIG), calculated in the latent space of NP based models. We perform a theoretical analysis and demonstrate that LIG reduces sample complexity compared with random sampling in high dimensions. We also conduct empirical studies on three complex spatiotemporal simulators for reaction diffusion, heat flow, and infectious disease. The results demonstrate that STNP outperforms the baselines in the offline learning setting and LIG achieves the state-of-the-art for Bayesian active learning.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3580305.3599300
- https://dl.acm.org/doi/pdf/10.1145/3580305.3599300
- OA Status
- gold
- Cited By
- 3
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385568448
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Raw OpenAlex JSON
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https://openalex.org/W4385568448Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3580305.3599300Digital Object Identifier
- Title
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Deep Bayesian Active Learning for Accelerating Stochastic SimulationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-04Full publication date if available
- Authors
-
Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose YuList of authors in order
- Landing page
-
https://doi.org/10.1145/3580305.3599300Publisher landing page
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https://dl.acm.org/doi/pdf/10.1145/3580305.3599300Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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-
goldOpen access status per OpenAlex
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https://dl.acm.org/doi/pdf/10.1145/3580305.3599300Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Machine learning, Surrogate model, Deep learning, Bayesian optimization, Artificial neural network, Bayesian probability, Bayesian inference, Active learning (machine learning)Top concepts (fields/topics) attached by OpenAlex
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-
3Total citation count in OpenAlex
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2025: 2, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.speed | 20 |
| abstract_inverted_index.three | 142 |
| abstract_inverted_index.(INP), | 43 |
| abstract_inverted_index.(LIG), | 107 |
| abstract_inverted_index.(STNP) | 89 |
| abstract_inverted_index.Latent | 104 |
| abstract_inverted_index.Neural | 41, 70, 87 |
| abstract_inverted_index.active | 31, 47, 79, 96, 174 |
| abstract_inverted_index.family | 73 |
| abstract_inverted_index.latent | 111 |
| abstract_inverted_index.models | 8, 18, 54 |
| abstract_inverted_index.random | 131 |
| abstract_inverted_index.sample | 127 |
| abstract_inverted_index.Process | 42, 71, 88 |
| abstract_inverted_index.complex | 143 |
| abstract_inverted_index.conduct | 138 |
| abstract_inverted_index.develop | 85 |
| abstract_inverted_index.models. | 116 |
| abstract_inverted_index.offline | 164 |
| abstract_inverted_index.perform | 118 |
| abstract_inverted_index.propose | 39, 99 |
| abstract_inverted_index.reduces | 126 |
| abstract_inverted_index.results | 155 |
| abstract_inverted_index.setting | 166 |
| abstract_inverted_index.studies | 140 |
| abstract_inverted_index.Bayesian | 46, 173 |
| abstract_inverted_index.achieves | 169 |
| abstract_inverted_index.analysis | 121 |
| abstract_inverted_index.compared | 129 |
| abstract_inverted_index.consists | 60 |
| abstract_inverted_index.disease. | 153 |
| abstract_inverted_index.epidemic | 7 |
| abstract_inverted_index.function | 77 |
| abstract_inverted_index.learning | 32, 48, 51, 165 |
| abstract_inverted_index.reaction | 147 |
| abstract_inverted_index.sampling | 132 |
| abstract_inverted_index.baselines | 161 |
| abstract_inverted_index.dynamics. | 94 |
| abstract_inverted_index.empirical | 139 |
| abstract_inverted_index.expensive | 11 |
| abstract_inverted_index.framework | 49 |
| abstract_inverted_index.function, | 103 |
| abstract_inverted_index.learning, | 97 |
| abstract_inverted_index.learning. | 80, 175 |
| abstract_inverted_index.modeling, | 83 |
| abstract_inverted_index.simulator | 93 |
| abstract_inverted_index.surrogate | 17, 53, 66, 82 |
| abstract_inverted_index.Stochastic | 0 |
| abstract_inverted_index.accelerate | 56 |
| abstract_inverted_index.approaches | 33 |
| abstract_inverted_index.calculated | 108 |
| abstract_inverted_index.complexity | 128 |
| abstract_inverted_index.diffusion, | 148 |
| abstract_inverted_index.infectious | 152 |
| abstract_inverted_index.simulators | 145 |
| abstract_inverted_index.stochastic | 27, 57 |
| abstract_inverted_index.Information | 105 |
| abstract_inverted_index.Interactive | 40 |
| abstract_inverted_index.acquisition | 76, 102 |
| abstract_inverted_index.components, | 63 |
| abstract_inverted_index.demonstrate | 123, 156 |
| abstract_inverted_index.dimensions. | 135 |
| abstract_inverted_index.outperforms | 159 |
| abstract_inverted_index.resolution. | 14 |
| abstract_inverted_index.simulations | 1, 28 |
| abstract_inverted_index.theoretical | 120 |
| abstract_inverted_index.fine-grained | 13 |
| abstract_inverted_index.large-scale, | 4 |
| abstract_inverted_index.simulations, | 23 |
| abstract_inverted_index.simulations. | 58 |
| abstract_inverted_index.underexplored | 36 |
| abstract_inverted_index.Spatiotemporal | 86 |
| abstract_inverted_index.age-structured | 6 |
| abstract_inverted_index.spatiotemporal | 65, 144 |
| abstract_inverted_index.computationally | 10 |
| abstract_inverted_index.spatiotemporal, | 5 |
| abstract_inverted_index.state-of-the-art | 171 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.8100000023841858 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.73420476 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |