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Empirical Inference Conference Paper 2019

Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks

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Empirical Inference
Julius von Kügelgen
  • Doctoral Researcher
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Empirical Inference
Paul Rubenstein
  • Doctoral Researcher
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Empirical Inference
Bernhard Schölkopf
  • Director
Kb optimalexperimental deisgn via bayesian optimisation active causal structure learning for gaussian process networks

Author(s): von Kügelgen, J. and Rubenstein, P. K. and Schölkopf, B. and Weller, A.
Book Title: NeurIPS 2019 Workshop Do the right thing: machine learning and causal inference for improved decision making
Year: 2019
Month: December
Day: 14
Bibtex Type: Conference Paper (conference)
Event Place: Vancouver, CA
State: Published
URL: http://tripods.cis.cornell.edu/neurips19_causalml/
Electronic Archiving: grant_archive
Links:
Attachments:

BibTex 

@conference{KueRubSchWel19,
  title = {Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks},
  booktitle = {NeurIPS 2019 Workshop Do the right thing: machine learning and causal inference for improved decision making},
  month = dec,
  year = {2019},
  slug = {kuerubschwel19},
  author = {von K{\"u}gelgen, J. and Rubenstein, P. K. and Sch{\"o}lkopf, B. and Weller, A.},
  url = {http://tripods.cis.cornell.edu/neurips19_causalml/},
  month_numeric = {12}
}