Demo Data from EDBT 2026 / reviewers in the wild / expert
Yanqiu Ruan
dblp:351/0720
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2023
0000-0002-5409-4634ORCID · 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 2021Theory of computation · 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.
| Theoretical computer science
1 paper |
Distributed computing theory · 100% |
Topics — the 1 heaviest of 1, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Distributed computing theory › distributed graph algorithms
congested clique |
0.7 | 1 | 2023 | A Nonparametric Approach with Marginals for Modeling Consumer Choice · EC 2023 |
Methods — techniques the papers use, named apart from their topics
non-parametric modeling · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | A Nonparametric Approach with Marginals for Modeling Consumer ChoiceabstractGiven data on choices made by consumers for different assortments, a key challenge is to develop parsimonious models that describe and predict consumer choice behavior. One such choice model is the marginal distribution model (MDM), which requires only the specification of the marginal distributions of the random utilities of the alternatives to explain choice data. Yanqiu Ruan, Xiaobo Li 0002, Karthyek Murthy, Karthik Natarajan |
EC | 1 |