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Learning directed acyclic graph (DAG) that describes the causality of observed data is a very challenging but important task.
On the evolution of random graphs
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Causation, prediction, and search
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Optimal structure identification with greedy search
Chickering, D. M. 2002 · 2002
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Bayesian approach to structure discovery in Bayesian networks
Friedman, N.; and Koller, D. 2003 · 2003
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Large-sample learning of Bayesian networks is NP-hard
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The max-min hill-climbing Bayesian network structure learning algorithm
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Causality
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Kernel-based Conditional Independence Test and Application in Causal Discovery
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Human-level control through deep reinforcement learning
Mnih, V.; Kavukcuoglu, K.; Silver, D.; Rusu, A. A.; Veness, J.; Bellemare, M. G.; Graves, A.; Riedmiller, M.; Fidjeland, A. K.; Ostrovski, G.; et al. 2015 · 2015
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Peters, J.; and Bühlmann, P. 2015 · 2015
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DAGs with no tears: Continuous optimization for structure learning
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DAG-GNN: DAG structure learning with graph neural networks
Yu, Y.; Chen, J.; Gao, T.; and Yu, M. 2019 · 2019
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Causal Discovery with Reinforcement Learning
Zhu, S.; Ng, I.; and Chen, Z. 2019 · 2019
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Ordering-based causal structure learning in the presence of latent variables
Bernstein, D.; Saeed, B.; Squires, C.; and Uhler, C. 2020 · 2020
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Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
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Raskutti, G.; and Uhler, C. 2018 · 2018
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Ordering-Based Causal Discovery with Reinforcement Learning
Wang, X.; Du, Y.; Zhu, S.; Ke, L.; Chen, Z.; Hao, J.; and Wang, J. 2021 · 2021
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Bayesian Structure Learning with Generative Flow Networks
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