Demo Data from EDBT 2026 / reviewers in the wild / expert
H. Peyton Young
dblp:22/4795 · also Peyton Young
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2007
—ORCID · unresolved
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 |
Multi-agent systems · 50% Learning theory · 50% | |
| Theoretical computer science
1 paper |
Algorithmic game theory and mechanism design · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Learning theory › computational learning theory
learnability |
0.1 | 1 | 2007 | The possible and the impossible in multi-agent learning · Artif. Intell. 2007 |
Knowledge, reasoning and agents › Multi-agent systems
multi-agent learning |
0.1 | 1 | 2007 | The possible and the impossible in multi-agent learning · Artif. Intell. 2007 |
Algorithmic game theory and mechanism design › multi-agent systems
multi-agent learning |
0.0 | 1 | 2007 | The possible and the impossible in multi-agent learning · Artif. Intell. 2007 |
Methods — techniques the papers use, named apart from their topics
learning theory · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2007 | The possible and the impossible in multi-agent learning
H. Peyton Young |
Artif. Intell. | 1 |