Non-IID learning

Longbing Cao

doi:10.1109/MIS.2022.3197949

Non-IID learning

Longbing Cao

Research output: Contribution to journal › Editorial

1 Citation (Scopus)

Abstract

Real-life AI systems are non-IID, i.e., their variables are unlikely independent and drawn from the same distribution. Instead, non-IIDness is a common characteristic and complexity of real-life systems, where variables, objects, and subsystems are coupled/interactive and heterogeneous. This issue highlights this important theme on Non-IID Learning with six feature articles. In addition, four columns highlight expert opinions on beyond i.i.d., trustworthy AI, data-driven predictive maintenance, and secrets for data science deployments, respectively.

Original language	English
Pages (from-to)	3-4
Number of pages	2
Journal	IEEE Intelligent Systems
Volume	37
Issue number	4
DOIs	https://doi.org/10.1109/MIS.2022.3197949
Publication status	Published - 2022
Externally published	Yes

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10.1109/MIS.2022.3197949

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title = "Non-IID learning",

abstract = "Real-life AI systems are non-IID, i.e., their variables are unlikely independent and drawn from the same distribution. Instead, non-IIDness is a common characteristic and complexity of real-life systems, where variables, objects, and subsystems are coupled/interactive and heterogeneous. This issue highlights this important theme on Non-IID Learning with six feature articles. In addition, four columns highlight expert opinions on beyond i.i.d., trustworthy AI, data-driven predictive maintenance, and secrets for data science deployments, respectively.",

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language = "English",

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AB - Real-life AI systems are non-IID, i.e., their variables are unlikely independent and drawn from the same distribution. Instead, non-IIDness is a common characteristic and complexity of real-life systems, where variables, objects, and subsystems are coupled/interactive and heterogeneous. This issue highlights this important theme on Non-IID Learning with six feature articles. In addition, four columns highlight expert opinions on beyond i.i.d., trustworthy AI, data-driven predictive maintenance, and secrets for data science deployments, respectively.

UR - https://www.scopus.com/pages/publications/85139297014

U2 - 10.1109/MIS.2022.3197949

DO - 10.1109/MIS.2022.3197949

M3 - Editorial

SN - 1541-1672

VL - 37

SP - 3

EP - 4

JO - IEEE Intelligent Systems

JF - IEEE Intelligent Systems

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ER -

Non-IID learning

Abstract

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