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

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

Julius von Kügelgen
Paul Kishan Rubenstein
Bernhard Schölkopf
Adrian Weller
    CML
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Papers citing "Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks"

12 / 12 papers shown
Title
InfoNCE: Identifying the Gap Between Theory and Practice
InfoNCE: Identifying the Gap Between Theory and Practice
E. Rusak
Patrik Reizinger
Attila Juhos
Oliver Bringmann
Roland S. Zimmermann
Wieland Brendel
71
7
0
28 Jun 2024
Targeted Sequential Indirect Experiment Design
Targeted Sequential Indirect Experiment Design
Elisabeth Ailer
Niclas Dern
Jason S. Hartford
Niki Kilbertus
58
2
0
30 May 2024
Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orders
Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orders
Christian Toth
Christian Knoll
Franz Pernkopf
Robert Peharz
CML
85
1
0
22 Feb 2024
Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models
Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models
Raj Agrawal
Tamara Broderick
Caroline Uhler
CML
19
17
0
15 Mar 2018
Budgeted Experiment Design for Causal Structure Learning
Budgeted Experiment Design for Causal Structure Learning
AmirEmad Ghassami
Saber Salehkaleybar
Negar Kiyavash
Elias Bareinboim
CML
61
62
0
11 Sep 2017
Probabilistic Active Learning of Functions in Structural Causal Models
Probabilistic Active Learning of Functions in Structural Causal Models
Paul Kishan Rubenstein
Ilya O. Tolstikhin
Philipp Hennig
Bernhard Schölkopf
TPM
CML
23
9
0
30 Jun 2017
Distinguishing cause from effect using observational data: methods and benchmarks
Distinguishing cause from effect using observational data: methods and benchmarks
Joris M. Mooij
J. Peters
Dominik Janzing
Jakob Zscheischler
Bernhard Schölkopf
CML
49
482
0
11 Dec 2014
Causal Discovery with Continuous Additive Noise Models
Causal Discovery with Continuous Additive Noise Models
Jonas Peters
Joris Mooij
Dominik Janzing
Bernhard Schölkopf
CML
75
563
0
26 Sep 2013
A Bayesian Approach to Learning Causal Networks
A Bayesian Approach to Learning Causal Networks
David Heckerman
CML
54
213
0
20 Feb 2013
Gaussian Process Networks
Gaussian Process Networks
N. Friedman
I. Nachman
GP
BDL
132
85
0
16 Jan 2013
Causal Discovery from Changes
Causal Discovery from Changes
Jin Tian
Judea Pearl
CML
74
165
0
10 Jan 2013
Almost Optimal Intervention Sets for Causal Discovery
Almost Optimal Intervention Sets for Causal Discovery
F. Eberhardt
CML
61
59
0
13 Jun 2012