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Xiaogang Su

dblp:28/7607 · DBLP profile ↗
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3ranked-venue papers
3as first author
0since 2021 · last 2012
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3 · 3 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
2 papers
Probabilistic and Bayesian machine learning · 89% Kernel, tree and ensemble methods · 11%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational science and engineering · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Probabilistic and Bayesian machine learning
causal inference		 0.112012
Facilitating score and causal inference trees for large observational studies · J. Mach. Learn. Res. 2012
Machine learning › Probabilistic and Bayesian machine learning
subgroup analysis		 0.112009
Subgroup Analysis via Recursive Partitioning · J. Mach. Learn. Res. 2009
Computational science and engineering
observational study		 0.012012
Facilitating score and causal inference trees for large observational studies · J. Mach. Learn. Res. 2012
Machine learning › Kernel, tree and ensemble methods
recursive partitioning		 0.012009
Subgroup Analysis via Recursive Partitioning · J. Mach. Learn. Res. 2009

Methods — techniques the papers use, named apart from their topics

recursive partitioning · 0.3propensity score · 0.3
YearPublicationVenuePosition
2012 Facilitating score and causal inference trees for large observational studies
Xiaogang Su, Joseph Kang, Juanjuan Fan, Richard A. Levine
 J. Mach. Learn. Res.1
2009 Subgroup Analysis via Recursive Partitioning
Xiaogang Su, Chih-Ling Tsai, Hansheng Wang 0002, David M. Nickerson, Bogong Li
 J. Mach. Learn. Res.1
2009 Tree-structured model diagnostics for linear regression
Xiaogang Su, Chih-Ling Tsai, Morgan C. Wang
 Mach. Learn.1