EDBT 2026 Demo / reviewers in the wild / expert
Ariel Shaulker
dblp:270/0805
· DBLP profile ↗
4ranked-venue papers
0as first author
4since 2021 · last 2025
0009-0003-2758-0578ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 4 · 4 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Bilateral Trade with Interdependent Values: Information vs. ApproximationabstractWelfare maximization in bilateral trade has been extensively studied in recent years. Previous literature obtained incentive-compatible approximation mechanisms only for the private values case. In this paper, we study welfare maximization in bilateral trade with interdependent values. Designing mechanisms for interdependent settings is much more challenging because the values of the players depend on the private information of the others, requiring complex belief updates and strategic inference. Shahar Dobzinski, Alon Eden, Kira Goldner, Ariel Shaulker, Thodoris Tsilivis |
EC | 4 |
| 2025 | Multi-parameter Mechanisms for Consumer Surplus Maximization
Tomer Ezra, Daniel Schoepflin 0001, Ariel Shaulker |
STOC | 3 |
| 2024 | Bilateral Trade with Correlated ValuesabstractWe study the bilateral trade problem where a seller owns a single indivisible item, and a potential buyer seeks to purchase it. Previous mechanisms for this problem only considered the case where the values of the buyer and the seller are drawn from independent distributions. In contrast, this paper studies bilateral trade mechanisms when the values are drawn from a joint distribution. We prove that the buyer-offering mechanism guarantees an approximation ratio of e/e−1 ≈ 1.582 to the social welfare even if the values are drawn from a joint distribution. The buyer-offering mechanism is Bayesian incentive compatible, but the seller has a dominant strategy. We prove the buyer-offering mechanism is optimal in the sense that no Bayesian mechanism where one of the players has a dominant strategy can obtain an approximation ratio better than e/e−1. We also show that no mechanism in which both sides have a dominant strategy can provide any constant approximation to the social welfare when the values are drawn from a joint distribution. Finally, we prove some impossibility results on the power of general Bayesian incentive compatible mechanisms. In particular, we show that no deterministic Bayesian incentive-compatible mechanism can provide an approximation ratio better than 1+ln2/2≈ 1.346. Shahar Dobzinski, Ariel Shaulker |
STOC | 2 |
| 2023 | Rigidity in Mechanism Design and Its Applications
Shahar Dobzinski, Ariel Shaulker |
ITCS | 2 |