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Dynamic causal Bayesian optimisation
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Aglietti, Virginia, Dhir, N., Gonzalez, J. and Damoulas, Theodoros (2021) Dynamic causal Bayesian optimisation. In: Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS 2021), Virtual, 6-14 Dec 2021. Published in: Advances in Neural Information Processing Systems, 34 pp. 10549-10560.
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WRAP-dynamic-causal-Bayesian-optimisation-Damoulas-2021.pdf - Accepted Version - Requires a PDF viewer. Download (800Kb) | Preview |
Official URL: https://proceedings.neurips.cc/paper/2021/hash/577...
Abstract
This paper studies the problem of performing a sequence of optimal interventions in a causal dynamical system where both the target variable of interest and the inputs evolve over time. This problem arises in a variety of domains e.g. system biology and operational research. Dynamic Causal Bayesian Optimization (DCBO) brings together ideas from sequential decision making, causal inference and Gaussian process (GP) emulation. DCBO is useful in scenarios where all causal effects in a graph are changing over time. At every time step DCBO identifies a local optimal intervention by integrating both observational and past interventional data collected from the system. We give theoretical results detailing how one can transfer interventional information across time steps and define a dynamic causal GP model which can be used to quantify uncertainty and find optimal interventions in practice. We demonstrate how DCBO identifies optimal interventions faster than competing approaches in multiple settings and applications.
Item Type: | Conference Item (Paper) | |||||||||||||||||||||
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Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||||||||||||||
Library of Congress Subject Headings (LCSH): | Bayesian statistical decision theory, Mathematical optimization, Neural networks (Computer science), Machine learning, Gaussian processes | |||||||||||||||||||||
Series Name: | NeurIPS Proceedings | |||||||||||||||||||||
Journal or Publication Title: | Advances in Neural Information Processing Systems | |||||||||||||||||||||
Publisher: | Curran Associates, Inc. | |||||||||||||||||||||
Official Date: | 2021 | |||||||||||||||||||||
Dates: |
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Volume: | 34 | |||||||||||||||||||||
Page Range: | pp. 10549-10560 | |||||||||||||||||||||
Status: | Peer Reviewed | |||||||||||||||||||||
Publication Status: | Published | |||||||||||||||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||||||||
Date of first compliant deposit: | 20 October 2021 | |||||||||||||||||||||
Date of first compliant Open Access: | 21 October 2021 | |||||||||||||||||||||
RIOXX Funder/Project Grant: |
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Conference Paper Type: | Paper | |||||||||||||||||||||
Title of Event: | Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS 2021) | |||||||||||||||||||||
Type of Event: | Conference | |||||||||||||||||||||
Location of Event: | Virtual | |||||||||||||||||||||
Date(s) of Event: | 6-14 Dec 2021 | |||||||||||||||||||||
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