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

arXiv

Empirical Inference

Julius von Kügelgen

Doctoral Researcher

Empirical Inference

Paul Rubenstein

Doctoral Researcher

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.
Links:	arXiv
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
Attachments:	Poster

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},
  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}
}