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Vasilis Varsamis

dblp:400/4657 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Algorithmic game theory and mechanism design › social choice
computational social choice
0.912025
Computing Voting Rules with Improvement Feedback · ICML 2025
Algorithmic game theory and mechanism design › social choice › computational social choice › voting rules
positional scoring rules
0.912025
Computing Voting Rules with Improvement Feedback · ICML 2025
Algorithmic game theory and mechanism design › social choice
preference aggregation
0.912025
Computing Voting Rules with Improvement Feedback · ICML 2025
Algorithmic game theory and mechanism design
social choice
0.912025
Computing Voting Rules with Improvement Feedback · ICML 2025
Algorithmic game theory and mechanism design › social choice › computational social choice
voting rules
0.912025
Computing Voting Rules with Improvement Feedback · ICML 2025
Distributed computing theory
impossibility results
0.312025
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
YearPublicationVenuePosition
2025 Computing Voting Rules with Improvement Feedback
abstract
Aggregating 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
ICML2