Demo Data from EDBT 2026 / reviewers in the wild / expert
Justin Kruger
dblp:151/3645
· DBLP profile ↗
3ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0001-8186-6073ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 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.
| Theoretical computer science
2 papers |
Algorithmic game theory and mechanism design · 100% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Algorithmic game theory and mechanism design
fair division |
1.0 | 2 | 2023 | Portioning using ordinal preferences: Fairness and efficiency · Artif. Intell. 2023 Portioning Using Ordinal Preferences: Fairness and Efficiency · IJCAI 2019 |
Algorithmic game theory and mechanism design › social choice
ordinal preferences |
1.0 | 2 | 2023 | Portioning using ordinal preferences: Fairness and efficiency · Artif. Intell. 2023 Portioning Using Ordinal Preferences: Fairness and Efficiency · IJCAI 2019 |
Algorithmic game theory and mechanism design
social choice |
1.0 | 2 | 2023 | Portioning using ordinal preferences: Fairness and efficiency · Artif. Intell. 2023 Portioning Using Ordinal Preferences: Fairness and Efficiency · IJCAI 2019 |
Algorithmic game theory and mechanism design › social choice
participatory budgeting |
0.7 | 1 | 2023 | Portioning using ordinal preferences: Fairness and efficiency · Artif. Intell. 2023 |
Methods — techniques the papers use, named apart from their topics
positional scoring rules · 0.4nash product · 0.4leximin · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Portioning using ordinal preferences: Fairness and efficiencyabstractA divisible public resource is to be divided among projects. We study rules that decide on a distribution of the budget when voters have ordinal preference rankings over projects. Examples of such portioning problems are participatory budgeting, time shares, and parliament elections. We introduce a family of rules for portioning, inspired by positional scoring rules. Rules in this family are given by a scoring vector (such as plurality or Borda) associating a positive value with each rank in a vote, and an aggregation function such as leximin or the Nash product. Our family contains well-studied rules, but most are new. We discuss computational and normative properties of our rules. We focus on fairness, and introduce the SD-core, a group fairness notion. Our Nash rules are in the SD-core, and the leximin rules satisfy individual fairness properties. Both are Pareto-efficient. Stéphane Airiau, Haris Aziz 0001, Ioannis Caragiannis, Justin Kruger, Jérôme Lang, Dominik Peters |
Artif. Intell. | 4 |
| 2019 | Portioning Using Ordinal Preferences: Fairness and EfficiencyabstractA public divisible resource is to be divided among projects. We study rules that decide on a distribution of the budget when voters have ordinal preference rankings over projects. Examples of such portioning problems are participatory budgeting, time shares, and parliament elections. We introduce a family of rules for portioning, inspired by positional scoring rules. Rules in this family are given by a scoring vector (such as plurality or Borda) associating a positive value with each rank in a vote, and an aggregation function such as leximin or the Nash product. Our family contains well-studied rules, but most are new. We discuss computational and normative properties of our rules. We focus on fairness, and introduce the SD-core, a group fairness notion. Our Nash rules are in the SD-core, and the leximin rules satisfy individual fairness properties. Both are Pareto-efficient. Stéphane Airiau, Haris Aziz 0001, Ioannis Caragiannis, Justin Kruger, Jérôme Lang, Dominik Peters |
IJCAI | 4 |
| 2014 | Empirical Analysis of Aggregation Methods for Collective Annotation
Ciyang Qing, Ulle Endriss, Raquel Fernández, Justin Kruger |
COLING | 4 |