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pro vyhledávání: '"Liu, Mingzhou"'
Causal discovery is crucial for understanding complex systems and informing decisions. While observational data can uncover causal relationships under certain assumptions, it often falls short, making active interventions necessary. Current methods,
Externí odkaz:
http://arxiv.org/abs/2406.10917
Deep neural networks have demonstrated impressive accuracy in supervised learning tasks. However, their lack of transparency makes it hard for humans to trust their results, especially in safe-critic domains such as healthcare. To address this issue,
Externí odkaz:
http://arxiv.org/abs/2310.01766
Assessing causal effects in the presence of unobserved confounding is a challenging problem. Existing studies leveraged proxy variables or multiple treatments to adjust for the confounding bias. In particular, the latter approach attributes the impac
Externí odkaz:
http://arxiv.org/abs/2309.17283
Distinguishing causal connections from correlations is important in many scenarios. However, the presence of unobserved variables, such as the latent confounder, can introduce bias in conditional independence testing commonly employed in constraint-b
Externí odkaz:
http://arxiv.org/abs/2305.05281
Inferring causal structures from time series data is the central interest of many scientific inquiries. A major barrier to such inference is the problem of subsampling, i.e., the frequency of measurement is much lower than that of causal influence. T
Externí odkaz:
http://arxiv.org/abs/2305.05276
Autor:
Wang, Chao, Liu, Mingzhou, Liu, Hongliang, Yang, Qiulin, Zhou, Chang-An, Song, Lei, Ma, Kui, Yue, Hairong
Publikováno v:
In Separation and Purification Technology 19 February 2025 354 Part 4
Context, as referred to situational factors related to the object of interest, can help infer the object's states or properties in visual recognition. As such contextual features are too diverse (across instances) to be annotated, existing attempts s
Externí odkaz:
http://arxiv.org/abs/2110.04042
A major barrier to deploying current machine learning models lies in their non-reliability to dataset shifts. To resolve this problem, most existing studies attempted to transfer stable information to unseen environments. Particularly, independent ca
Externí odkaz:
http://arxiv.org/abs/2107.01876
Publikováno v:
In Advanced Engineering Informatics January 2024 59
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