Demo Data from EDBT 2026 / reviewers in the wild / expert
Atila Abdulkadiroglu
dblp:48/11408
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2025
0000-0003-0450-2967ORCID · 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 |
Algorithmic game theory and mechanism design · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Algorithmic game theory and mechanism design
distributional objectives |
0.9 | 1 | 2025 | Market Design for Distributional Objectives in Allocation Problems: An Axiomatic Approach · EC 2025 |
Algorithmic game theory and mechanism design
fair division |
0.9 | 1 | 2025 | Market Design for Distributional Objectives in Allocation Problems: An Axiomatic Approach · EC 2025 |
Algorithmic game theory and mechanism design
market design |
0.9 | 1 | 2025 | Market Design for Distributional Objectives in Allocation Problems: An Axiomatic Approach · EC 2025 |
Algorithmic game theory and mechanism design › matching
matching and assignment |
0.9 | 1 | 2025 | Market Design for Distributional Objectives in Allocation Problems: An Axiomatic Approach · EC 2025 |
Algorithmic game theory and mechanism design › market design › matching markets
school choice |
0.9 | 1 | 2025 | Market Design for Distributional Objectives in Allocation Problems: An Axiomatic Approach · EC 2025 |
Methods — techniques the papers use, named apart from their topics
priority-based assignment · 0.9axiomatic analysis · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Market Design for Distributional Objectives in Allocation Problems: An Axiomatic ApproachabstractWe study a many-to-one assignment problem, where a set of applicants need to be assigned to a set of schools with limited seats. We formulate distributional objectives by a set of standard axioms. Axiom 1 has two parts. First, the number of applicants of each type at each school should be less than the corresponding quota. Second, whenever the number of applicants of a given type assigned to a school is less than the corresponding reserve, then it should be that no other applicant of that type prefers the school to their own assignment. Axiom 2 requires that whenever a school has available seats, it should be that there is no applicant that prefers that school to their own assignment. Finally, we say that an applicant a violates the priority of another applicant b at school s, if b prefers s to their own assignment, and b has a higher priority at that school than a. In this case, the triplet (a, b, s) is called a priority violation instance. Axiom 3 requires that there are no priority violation instances including two applicants of the same type. Atila Abdulkadiroglu, Aram Grigoryan |
EC | 1 |