Policy-Based Bayesian Active Causal Discovery with Deep Reinforcement Learning-Reference-Cited by-同舟云学术

Policy-Based Bayesian Active Causal Discovery with Deep Reinforcement Learning

Published:2024-08-24 Issue: Volume:33 Page:839-850
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Gao Heyang¹^ORCID,Sun Zexu¹^ORCID,Yang Hao¹^ORCID,Chen Xu¹^ORCID

Affiliation:

1. Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China

Funder

National Natural Science Foundation of China

Public Computing Cloud, Renmin University of China

Intelligent Social Governance Platform, Major Innovation & Planning Interdisciplinary Platform for the Double First-Class Initiative, Renmin University of China

fund for building world-class universities (disciplines) of Renmin University of China

National Key R&D Program of China

Beijing Outstanding Young Scientist Program

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671705

Reference53 articles.

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2. Yashas Annadani, Panagiotis Tigas, Desi R Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, and Stefan Bauer. 2023. Differentiable Multi-Target Causal Bayesian Experimental Design. arXiv preprint arXiv:2302.10607 (2023).

3. Albert-László Barabási and Réka Albert. 1999. Emergence of scaling in random networks. science, Vol. 286, 5439 (1999), 509--512.

4. Tom Blau, Edwin V Bonilla, Iadine Chades, and Amir Dezfouli. 2022. Optimizing sequential experimental design with deep reinforcement learning. In International Conference on Machine Learning. PMLR, 2107--2128.