Demo Data from EDBT 2026 / reviewers in the wild / expert
Andrew F. Dreher
dblp:384/0512
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2024
0009-0007-1972-2336ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Databases, data management, data science and information retrieval · 1 · 1 first-author · 1 since 2021
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 |
Learning theory · 50% Reinforcement learning · 25% Probabilistic and Bayesian machine learning · 25% | |
| Databases, data mining, and information retrieval
1 paper |
Data mining · 100% |
Topics — the 3 heaviest of 5, 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
bradley-terry-luce model |
0.8 | 1 | 2024 | Estimated Judge Reliabilities for Weighted Bradley-Terry-Luce Are Not Reliable · KDD 2024 |
Machine learning › Learning theory › pairwise learning
pairwise comparison |
0.8 | 1 | 2024 | Estimated Judge Reliabilities for Weighted Bradley-Terry-Luce Are Not Reliable · KDD 2024 |
Machine learning › Reinforcement learning
preference learning |
0.8 | 1 | 2024 | Estimated Judge Reliabilities for Weighted Bradley-Terry-Luce Are Not Reliable · KDD 2024 |
Methods — techniques the papers use, named apart from their topics
weighted estimation · 1.5bradley-terry-luce · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Estimated Judge Reliabilities for Weighted Bradley-Terry-Luce Are Not ReliableabstractThere are many applications for which we want to learn a latent scale for subjective properties, such as the excitement of a photo or the legibility of a font; however, obtaining human-labeled data is costly and time-consuming. One oft-used method for acquiring these labels, despite the cost being quadratic in the number of items, is the method of pairwise comparisons since this method minimizes the effect of biases and generally can be used effectively outside of a controlled environment. Andrew F. Dreher, Etienne Vouga, Donald S. Fussell |
KDD | 1 |