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

9 October 2019

Julius von Kügelgen

Paul Kishan Rubenstein

Bernhard Schölkopf

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

16 / 16 papers shown

Title
Graph Agnostic Causal Bayesian Optimisation Sumantrak Mukherjee Mengyan Zhang Seth Flaxman Sebastian Vollmer CML 97 0 0 05 Nov 2024
InfoNCE: Identifying the Gap Between Theory and Practice E. Rusak Patrik Reizinger Attila Juhos Oliver Bringmann Roland S. Zimmermann Wieland Brendel 130 11 0 28 Jun 2024
Targeted Sequential Indirect Experiment Design Elisabeth Ailer Niclas Dern Jason S. Hartford Niki Kilbertus 111 3 0 30 May 2024
Stopping Bayesian Optimization with Probabilistic Regret Bounds James T. Wilson 65 4 0 26 Feb 2024
Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orders Christian Toth Christian Knoll Franz Pernkopf Robert Peharz CML 155 1 0 22 Feb 2024
Bayesian Causal Inference with Gaussian Process Networks Enrico Giudice Jack Kuipers G. Moffa 80 1 0 01 Feb 2024
Estimation of Counterfactual Interventions under Uncertainties Juliane Weilbach S. Gerwinn M. Kandemir Martin Fraenzle 77 0 0 15 Sep 2023
Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network T. Deleu Mizu Nishikawa-Toomey Jithendaraa Subramanian Nikolay Malkin Laurent Charlin Yoshua Bengio BDL 126 46 0 30 May 2023
Modern Bayesian Experimental Design Tom Rainforth Adam Foster Desi R. Ivanova Freddie Bickford-Smith 119 88 0 28 Feb 2023
Differentiable Multi-Target Causal Bayesian Experimental Design Yashas Annadani P. Tigas Desi R. Ivanova Andrew Jesson Y. Gal Adam Foster Stefan Bauer BDL CML 80 13 0 21 Feb 2023
Active Bayesian Causal Inference Christian Toth Lars Lorch Christian Knoll Andreas Krause Franz Pernkopf Robert Peharz Julius von Kügelgen 83 27 0 04 Jun 2022
Causal Structure Learning: a Combinatorial Perspective C. Squires Caroline Uhler CML 120 47 0 02 Jun 2022
Interventions, Where and How? Experimental Design for Causal Models at Scale P. Tigas Yashas Annadani Andrew Jesson Bernhard Schölkopf Y. Gal Stefan Bauer CML 150 50 0 03 Mar 2022
DiBS: Differentiable Bayesian Structure Learning Lars Lorch Jonas Rothfuss Bernhard Schölkopf Andreas Krause 101 91 0 25 May 2021
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach Amir-Hossein Karimi Julius von Kügelgen Bernhard Schölkopf Isabel Valera CML 106 181 0 11 Jun 2020
Gryffin: An algorithm for Bayesian optimization of categorical variables informed by expert knowledge Florian Hase Matteo Aldeghi Riley J. Hickman L. Roch Alán Aspuru-Guzik 106 110 0 26 Mar 2020