Lawrence Shih

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

dblp:27/1873 · DBLP profile ↗

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2ranked-venue papers

1as first author

0since 2021 · last 2003

—ORCID · none

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

Artificial intelligence and machine learning · 2 · 1 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.

Databases, data mining, and information retrieval

2 papers

Data mining · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

Topic	Papers	Last	Evidence papers
Data mining data reduction	1	2003	Text Bundling: Statistics Based Data-Reduction · ICML 2003
Data mining › text mining › text classification naive bayes text classification	1	2003	Tackling the Poor Assumptions of Naive Bayes Text Classifiers · ICML 2003
Data mining › text mining text classification	1	2003	Tackling the Poor Assumptions of Naive Bayes Text Classifiers · ICML 2003

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

statistics-based data reduction · 0.0naive bayes · 0.0

Year	Publication	Venue	Position
2003	Tackling the Poor Assumptions of Naive Bayes Text Classifiers Jason Rennie, Lawrence Shih, Jaime Teevan, David R. Karger	ICML	2
2003	Text Bundling: Statistics Based Data-Reduction Lawrence Shih, Jason Rennie, Yu-Han Chang, David R. Karger	ICML	1