VLDB 2026 Research / reviewers in the wild / expert
Amitai Frey
dblp:367/3939
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0000-8576-1631ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Theory of computation · 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 · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Algorithmic game theory and mechanism design › mechanism design › auction design
double auction |
0.8 | 1 | 2024 | Learning to Maximize Gains From Trade in Small Markets · EC 2024 |
Algorithmic game theory and mechanism design › market design
gains from trade |
0.8 | 1 | 2024 | Learning to Maximize Gains From Trade in Small Markets · EC 2024 |
Algorithmic game theory and mechanism design
mechanism design |
0.8 | 1 | 2024 | Learning to Maximize Gains From Trade in Small Markets · EC 2024 |
Algorithmic game theory and mechanism design › mechanism design › mechanism design with uncertainty
sample-based mechanism design |
0.2 | 1 | 2024 | Learning to Maximize Gains From Trade in Small Markets · EC 2024 |
Methods — techniques the papers use, named apart from their topics
learning theory · 0.8impossibility result · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Learning to Maximize Gains From Trade in Small MarketsabstractWe study the problem of designing a two-sided market (double auction) to maximize the gains from trade (social welfare) under the constraints of (dominant-strategy) incentive compatibility and budget-balance. Our goal is to do so for an unknown distribution from which we are given a polynomial number of samples. Our first result is a general impossibility for the case of correlated distributions of values even between just one seller and two buyers, in contrast to the case of one seller and one buyer (bilateral trade) where this is possible. Our second result is an efficient learning algorithm for one seller and two buyers in the case of independent distributions which is based on a novel algorithm for computing optimal mechanisms for finitely supported and explicitly given independent distributions. Both results rely heavily on characterizations of (dominant-strategy) incentive compatible mechanisms that are strongly budget-balanced. Moshe Babaioff, Amitai Frey, Noam Nisan |
EC | 2 |