Demo Data from EDBT 2026 / reviewers in the wild / expert
Peter Barlett
dblp:73/2603
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1997
—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 |
Kernel, tree and ensemble methods · 40% Learning theory · 40% Multi-agent systems · 20% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Kernel, tree and ensemble methods › ensemble learning
boosting |
0.0 | 1 | 1997 | Boosting the margin: A new explanation for the effectiveness of voting methods · ICML 1997 |
Machine learning › Kernel, tree and ensemble methods
ensemble learning |
0.0 | 1 | 1997 | Boosting the margin: A new explanation for the effectiveness of voting methods · ICML 1997 |
Machine learning › Learning theory
generalization bounds |
0.0 | 1 | 1997 | Boosting the margin: A new explanation for the effectiveness of voting methods · ICML 1997 |
Machine learning › Learning theory › generalization bounds
margin theory |
0.0 | 1 | 1997 | Boosting the margin: A new explanation for the effectiveness of voting methods · ICML 1997 |
Knowledge, reasoning and agents › Multi-agent systems › social choice
voting methods |
0.0 | 1 | 1997 | Boosting the margin: A new explanation for the effectiveness of voting methods · ICML 1997 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1997 | Boosting the margin: A new explanation for the effectiveness of voting methods
Robert E. Schapire, Yoav Freund, Peter Barlett, Wee Sun Lee |
ICML | 3 |