Demo Data from EDBT 2026 / reviewers in the wild / expert
Eitan Greenshtein
dblp:23/4537
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2009
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 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.
| Artificial intelligence
1 paper |
Probabilistic and Bayesian machine learning · 87% Learning theory · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › bayesian inference
empirical bayes |
0.1 | 1 | 2009 | Application of Non Parametric Empirical Bayes Estimation to High Dimensional Classification · J. Mach. Learn. Res. 2009 |
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › bayesian inference › empirical bayes
nonparametric empirical bayes |
0.1 | 1 | 2009 | Application of Non Parametric Empirical Bayes Estimation to High Dimensional Classification · J. Mach. Learn. Res. 2009 |
Machine learning › Learning theory › classification
high-dimensional classification |
0.0 | 1 | 2009 | Application of Non Parametric Empirical Bayes Estimation to High Dimensional Classification · J. Mach. Learn. Res. 2009 |
Methods — techniques the papers use, named apart from their topics
empirical bayes estimation · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2009 | Application of Non Parametric Empirical Bayes Estimation to High Dimensional Classification
Eitan Greenshtein |
J. Mach. Learn. Res. | 1 |