Demo Data from EDBT 2026 / reviewers in the wild / expert
Marta Crispino
dblp:186/0095
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2017
—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 |
Reinforcement learning · 50% Probabilistic and Bayesian machine learning · 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 › Probabilistic and Bayesian machine learning › structured prediction › ranking model
mallows model |
0.3 | 1 | 2017 | Probabilistic preference learning with the Mallows rank model · J. Mach. Learn. Res. 2017 |
Machine learning › Reinforcement learning
preference learning |
0.3 | 1 | 2017 | Probabilistic preference learning with the Mallows rank model · J. Mach. Learn. Res. 2017 |
Algorithmic game theory and mechanism design › social choice
rank aggregation |
0.1 | 1 | 2017 | Probabilistic preference learning with the Mallows rank model · J. Mach. Learn. Res. 2017 |
Methods — techniques the papers use, named apart from their topics
mallows model · 0.6bayesian inference · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Probabilistic preference learning with the Mallows rank model
Valeria Vitelli, Øystein Sørensen, Marta Crispino, Arnoldo Frigessi, Elja Arjas |
J. Mach. Learn. Res. | 3 |