Demo Data from VLDB 2026 / reviewers in the wild / expert
Vasilis Varsamis
dblp:400/4657
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 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 · 94% Distributed computing theory · 6% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Algorithmic game theory and mechanism design › social choice
computational social choice |
0.9 | 1 | 2025 | Computing Voting Rules with Improvement Feedback · ICML 2025 |
Algorithmic game theory and mechanism design › social choice › computational social choice › voting rules
positional scoring rules |
0.9 | 1 | 2025 | Computing Voting Rules with Improvement Feedback · ICML 2025 |
Algorithmic game theory and mechanism design › social choice
preference aggregation |
0.9 | 1 | 2025 | Computing Voting Rules with Improvement Feedback · ICML 2025 |
Algorithmic game theory and mechanism design
social choice |
0.9 | 1 | 2025 | Computing Voting Rules with Improvement Feedback · ICML 2025 |
Algorithmic game theory and mechanism design › social choice › computational social choice
voting rules |
0.9 | 1 | 2025 | Computing Voting Rules with Improvement Feedback · ICML 2025 |
Distributed computing theory
impossibility results |
0.3 | 1 | 2025 | Computing Voting Rules with Improvement Feedback · ICML 2025 |
Methods — techniques the papers use, named apart from their topics
positional scoring rules · 0.9condorcet consistency · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Computing Voting Rules with Improvement FeedbackabstractAggregating preferences under incomplete or constrained feedback is a fundamental problem in social choice and related domains. While prior work has established strong impossibility results for pairwise comparisons, this paper extends the inquiry to improvement feedback, where voters express incremental adjustments rather than complete preferences. We provide a complete characterization of the positional scoring rules that can be computed given improvement feedback. Interestingly, while plurality is learnable under improvement feedback—unlike with pairwise feedback—strong impossibility results persist for many other positional scoring rules. Furthermore, we show that improvement feedback, unlike pairwise feedback, does not suffice for the computation of any Condorcet-consistent rule. We complement our theoretical findings with experimental results, providing further insights into the practical implications of improvement feedback for preference aggregation. Evi Micha, Vasilis Varsamis |
ICML | 2 |