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Demo Data from VLDB 2026 / reviewers in the wild / expert

James Taggart

dblp:223/4357 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

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

Artificial intelligence and machine learning · 1

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
1 paper
Representation and self-supervised learning · 50% Deep learning architectures and training · 50%
Theoretical computer science
1 paper
Mathematical optimization · 100%

Topics — the 2 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Deep learning architectures and training › neural network training
neuroevolution		 0.412019
Learning concise representations for regression by evolving networks of trees · ICLR (Poster) 2019
Mathematical optimization › statistical estimation
regression		 0.112019
Learning concise representations for regression by evolving networks of trees · ICLR (Poster) 2019

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

tree networks · 0.8evolutionary algorithm · 0.8
YearPublicationVenuePosition
2019 Learning concise representations for regression by evolving networks of trees
William G. La Cava, Tilak Raj Singh, James Taggart, Srinivas Suri, Jason H. Moore
 ICLR (Poster)3