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pro vyhledávání: '"Yu, Kui"'
Multi-label feature selection serves as an effective mean for dealing with high-dimensional multi-label data. To achieve satisfactory performance, existing methods for multi-label feature selection often require the centralization of substantial data
Externí odkaz:
http://arxiv.org/abs/2403.06419
An essential and challenging problem in causal inference is causal effect estimation from observational data. The problem becomes more difficult with the presence of unobserved confounding variables. The front-door adjustment is a practical approach
Externí odkaz:
http://arxiv.org/abs/2310.01937
Fair feature selection for classification decision tasks has recently garnered significant attention from researchers. However, existing fair feature selection algorithms fall short of providing a full explanation of the causal relationship between f
Externí odkaz:
http://arxiv.org/abs/2306.10336
An essential problem in causal inference is estimating causal effects from observational data. The problem becomes more challenging with the presence of unobserved confounders. When there are unobserved confounders, the commonly used back-door adjust
Externí odkaz:
http://arxiv.org/abs/2304.11969
Causal structure learning has been extensively studied and widely used in machine learning and various applications. To achieve an ideal performance, existing causal structure learning algorithms often need to centralize a large amount of data from m
Externí odkaz:
http://arxiv.org/abs/2211.06919
This paper studies the problem of estimating the contributions of features to the prediction of a specific instance by a machine learning model and the overall contribution of a feature to the model. The causal effect of a feature (variable) on the p
Externí odkaz:
http://arxiv.org/abs/2206.11529
Instrumental variable (IV) is a powerful approach to inferring the causal effect of a treatment on an outcome of interest from observational data even when there exist latent confounders between the treatment and the outcome. However, existing IV met
Externí odkaz:
http://arxiv.org/abs/2206.01931
Recent years have witnessed increasing interest in few-shot knowledge graph completion (FKGC), which aims to infer unseen query triples for a few-shot relation using a few reference triples about the relation. The primary focus of existing FKGC metho
Externí odkaz:
http://arxiv.org/abs/2203.11639
Local-to-global learning approach plays an essential role in Bayesian network (BN) structure learning. Existing local-to-global learning algorithms first construct the skeleton of a DAG (directed acyclic graph) by learning the MB (Markov blanket) or
Externí odkaz:
http://arxiv.org/abs/2112.10369
Autor:
Ling, Ziji, Guo, Songsong, Xie, Hanyu, Chen, Xinyu, Yu, Kui, Jiang, Hongbing, Xu, Rongyao, Wu, Yunong, Zheng, Kai
Publikováno v:
In Applied Materials Today June 2024 38