Hidden Variable Selection
Hidden Variable Selection is a methodological framework in statistical modeling and machine learning that identifies latent or unobserved variables influencing observed data, distinguishing itself by systematically selecting such hidden factors to improve model interpretability and predictive accuracy.
7
Publications
547
Citations
19
Authors
14
Institutions
Publications per year
2017–2026
2
Parents
7
Automated discovery of fundamental variables hidden in experimental data
Boyuan Chen, Kuang Huang, Sunand Raghupathi et al. · Nature Computational Science · 2022 · 115 citations
Neural network design for short-term load forecasting
W. Charytoniuk, M.S. Chen · 2002 · 43 citations
Computing, Data Science, Forecasting +15
1–5 of 7
19
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14

Howard Hughes Medical Institute
Chevy Chase, United States
1 h-index
1 publications
51 citations
Nanyang Technological University
Singapore, Singapore
1 h-index
3 publications
423 citations
Columbia University
New York, United States
1 h-index
6 publications
690 citations
Courant Institute of Mathematical Sciences
New York, United States
1 h-index
1 publications
51 citations

Philadelphia University
Philadelphia, United States
1 h-index
2 publications
308 citations

State Grid Corporation of China (China)
Beijing, China
1 h-index
1 publications
14 citations
Dalian University of Technology
Dalian, China
1 h-index
2 publications
58 citations
Nanjing Agricultural University
Nanjing, China
1 h-index
1 publications
14 citations
1–8 of 14
2 publications · 170 citations
2
h-index
1 publications · 14 citations
1
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1 publications · 115 citations
1
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1 publications · 154 citations
1
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1 publications · 51 citations
1
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