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dc.contributor.authorJasra, Ajay
dc.contributor.authorLaw, Kody J. H.
dc.contributor.authorTarakanov, Alexander
dc.contributor.authorYu, Fangyuan
dc.date.accessioned2022-05-25T05:24:11Z
dc.date.available2021-07-14T06:32:36Z
dc.date.available2022-05-25T05:24:11Z
dc.date.issued2022-03-10
dc.identifier.citationJasra, A., Law, K. J. H., Tarakanov, A., & Yu, F. (2022). Randomized Multilevel Monte Carlo for Embarrassingly Parallel Inference. Communications in Computer and Information Science, 3–21. https://doi.org/10.1007/978-3-030-96498-6_1
dc.identifier.isbn9783030964979
dc.identifier.isbn9783030964986
dc.identifier.issn1865-0929
dc.identifier.issn1865-0937
dc.identifier.doi10.1007/978-3-030-96498-6_1
dc.identifier.urihttp://hdl.handle.net/10754/670196
dc.description.abstractThis position paper summarizes a recently developed research program focused on inference in the context of data centric science and engineering applications, and forecasts its trajectory forward over the next decade. Often one endeavours in this context to learn complex systems in order to make more informed predictions and high stakes decisions under uncertainty. Some key challenges which must be met in this context are robustness, generalizability, and interpretability. The Bayesian framework addresses these three challenges, while bringing with it a fourth, undesirable feature: it is typically far more expensive than its deterministic counterparts. In the 21st century, and increasingly over the past decade, a growing number of methods have emerged which allow one to leverage cheap low-fidelity models in order to precondition algorithms for performing inference with more expensive models and make Bayesian inference tractable in the context of high-dimensional and expensive models. Notable examples are multilevel Monte Carlo (MLMC), multi-index Monte Carlo (MIMC), and their randomized counterparts (rMLMC), which are able to provably achieve a dimension-independent (including ∞- dimension) canonical complexity rate with respect to mean squared error (MSE) of 1/MSE. Some parallelizability is typically lost in an inference context, but recently this has been largely recovered via novel double randomization approaches. Such an approach delivers independent and identically distributed samples of quantities of interest which are unbiased with respect to the infinite resolution target distribution. Over the coming decade, this family of algorithms has the potential to transform data centric science and engineering, as well as classical machine learning applications such as deep learning, by scaling up and scaling out fully Bayesian inference.
dc.description.sponsorshipKJHL and AT were supported by The Alan Turing Institute under the EPSRC grant EP/N510129/1. AJ and FY acknowledge KAUST baseline support.
dc.publisherSpringer International Publishing
dc.relation.urlhttps://link.springer.com/10.1007/978-3-030-96498-6_1
dc.rightsArchived with thanks to Springer International Publishing
dc.subjectRandomization Methods
dc.subjectMarkov chain Monte Carlo
dc.subjectBayesian Inference
dc.titleRandomized Multilevel Monte Carlo for Embarrassingly Parallel Inference
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955, Kingdom of Saudi Arabia
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.contributor.departmentStatistics
dc.rights.embargodate2023-03-10
dc.conference.date2021-10-18 to 2021-10-20
dc.conference.name21st Smoky Mountains Computational Sciences and Engineering Conference, SMC 2021
dc.conference.locationVirtual, Online
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Mathematics, University of Manchester, Manchester, M13 9PL, UK
dc.identifier.volume1512 CCIS
dc.identifier.pages3-21
dc.identifier.arxivid2107.01913
kaust.personJasra, Ajay
kaust.personYu, Fangyuan
dc.identifier.eid2-s2.0-85127041719
refterms.dateFOA2021-07-14T06:33:00Z
kaust.acknowledged.supportUnitBaseline support.


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