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pro vyhledávání: '"Chobtham, Kiattikun"'
One of the challenges practitioners face when applying structure learning algorithms to their data involves determining a set of hyperparameters; otherwise, a set of hyperparameter defaults is assumed. The optimal hyperparameter configuration often d
Externí odkaz:
http://arxiv.org/abs/2306.13932
Autor:
Constantinou, Anthony, Kitson, Neville K., Liu, Yang, Chobtham, Kiattikun, Hashemzadeh, Arian, Nanavati, Praharsh A., Mbuvha, Rendani, Petrungaro, Bruno
Causal machine learning (ML) algorithms recover graphical structures that tell us something about cause-and-effect relationships. The causal representation praovided by these algorithms enables transparency and explainability, which is necessary for
Externí odkaz:
http://arxiv.org/abs/2305.03859
Discovering and parameterising latent confounders represent important and challenging problems in causal structure learning and density estimation respectively. In this paper, we focus on both discovering and learning the distribution of latent confo
Externí odkaz:
http://arxiv.org/abs/2206.05490
In Bayesian Networks (BNs), the direction of edges is crucial for causal reasoning and inference. However, Markov equivalence class considerations mean it is not always possible to establish edge orientations, which is why many BN structure learning
Externí odkaz:
http://arxiv.org/abs/2112.10574
Learning the structure of a Bayesian Network (BN) with score-based solutions involves exploring the search space of possible graphs and moving towards the graph that maximises a given objective function. Some algorithms offer exact solutions that gua
Externí odkaz:
http://arxiv.org/abs/2112.00398
Bayesian Networks (BNs) have become increasingly popular over the last few decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology, epidemiology, economics and the social sciences. This is especially true in real-w
Externí odkaz:
http://arxiv.org/abs/2109.11415
Latent variables may lead to spurious relationships that can be misinterpreted as causal relationships. In Bayesian Networks (BNs), this challenge is known as learning under causal insufficiency. Structure learning algorithms that assume causal insuf
Externí odkaz:
http://arxiv.org/abs/2005.14381
Numerous Bayesian Network (BN) structure learning algorithms have been proposed in the literature over the past few decades. Each publication makes an empirical or theoretical case for the algorithm proposed in that publication and results across stu
Externí odkaz:
http://arxiv.org/abs/2005.09020
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Publikováno v:
In International Journal of Approximate Reasoning December 2022 151:292-321