Variational Bayesian Optimal Experimental Design. Foster, A., Jankowiak, M., Bingham, E., Horsfall, P., Teh, Y. W., Rainforth, T., & Goodman, N. D. In Wallach, H. M., Larochelle, H., Beygelzimer, A., d'Alché-Buc , F., Fox, E. B., & Garnett, R., editors, Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, pages 14036–14047, 2019.
Variational Bayesian Optimal Experimental Design [link]Paper  bibtex   
@inproceedings{DBLP:conf/nips/FosterJBHTRG19,
  author    = {Adam Foster and
               Martin Jankowiak and
               Eli Bingham and
               Paul Horsfall and
               Yee Whye Teh and
               Tom Rainforth and
               Noah D. Goodman},
  editor    = {Hanna M. Wallach and
               Hugo Larochelle and
               Alina Beygelzimer and
               Florence d'Alch{\'{e}}{-}Buc and
               Emily B. Fox and
               Roman Garnett},
  title     = {Variational Bayesian Optimal Experimental Design},
  booktitle = {Advances in Neural Information Processing Systems 32: Annual Conference
               on Neural Information Processing Systems 2019, NeurIPS 2019, December
               8-14, 2019, Vancouver, BC, Canada},
  pages     = {14036--14047},
  year      = {2019},
  url       = {https://proceedings.neurips.cc/paper/2019/hash/d55cbf210f175f4a37916eafe6c04f0d-Abstract.html},
  timestamp = {Thu, 21 Jan 2021 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/conf/nips/FosterJBHTRG19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

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