Demo Data from SIGIR 2026 / reviewers in the wild / expert
Ran Eilat
dblp:91/11120
· DBLP profile ↗
3ranked-venue papers
2as first author
3since 2021 · last 2025
0000-0001-8650-6026ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 2 first-author · 3 since 2021Theory of computation · 3 · 2 first-author · 3 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
3 papers |
Algorithmic game theory and mechanism design · 100% | |
| Network and information security
1 paper |
Privacy and data protection · 100% | |
| Artificial intelligence
1 paper |
Probabilistic and Bayesian machine learning · 100% |
Topics — the 6 heaviest of 8, 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 |
0.9 | 1 | 2025 | Privacy Preserving Auctions · EC 2025 |
Algorithmic game theory and mechanism design › auction theory › auction mechanism
privacy-preserving auction |
0.9 | 1 | 2025 | Privacy Preserving Auctions · EC 2025 |
Algorithmic game theory and mechanism design › auction theory › auction mechanism
strategy-proof auction |
0.9 | 1 | 2025 | Privacy Preserving Auctions · EC 2025 |
Algorithmic game theory and mechanism design › mechanism design › information design
bayesian persuasion |
0.8 | 1 | 2024 | The Core of Bayesian persuasion · EC 2024 |
Algorithmic game theory and mechanism design › mechanism design › information design
information structure design |
0.8 | 1 | 2024 | The Core of Bayesian persuasion · EC 2024 |
Machine learning › Probabilistic and Bayesian machine learning › statistical inference
bayesian inference |
0.2 | 1 | 2024 | The Core of Bayesian persuasion · EC 2024 |
Methods — techniques the papers use, named apart from their topics
mechanism design · 1.7information theory · 1.7convex analysis · 1.5bayesian persuasion · 1.5game-theoretic modeling · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Privacy Preserving AuctionsabstractIn many auction settings the auctioneer must disclose the identity of the winner and the price he pays. We characterize the auction that minimizes the winner's privacy loss among those that maximize total surplus or the seller's revenue, and are strategy-proof. Privacy loss is measured with respect to what an outside observer learns from the disclosed price, and is quantified by the mutual information between the price and the winner's willingness to pay. When only interim individual-rationality is required, the most privacy preserving auction involves stochastic ex-post payments. Under ex-post individual rationality, and assuming the bidders' type distribution exhibits a monotone hazard rate, privacy loss is minimized by the second-price auction with deterministic payments. Ran Eilat, Kfir Eliaz, Xiaosheng Mu |
EC | 1 |
| 2024 | The Core of Bayesian persuasionabstractAn analyst observes the frequency with which a decision maker (henceforth, DM) takes actions, but not the frequency with which she takes actions conditional on a payoff relevant state. In this setting, we characterize the information structures that rationalize the marginal distribution over actions as the result of the DM first observing the information structure's realization and then choosing actions optimally. Our main result identifies a core condition that must be satisfied between the marginal action distribution and the distribution over the DM's posterior beliefs for the information structure to rationalize the DM's choices. Laura Doval, Ran Eilat |
EC | 2 |
| 2023 | Opportunity Hunters: A Model of Competitive Sequential InspectionsabstractThere are many economic situations in which players compete to identify an uncertain event, or an "opportunity." Once an opportunity appears, seizing it quickly is critical. For example, two firms may consider the introduction of a new technology into a market, which may either be ripe for the technology or not. The first firm to identify that the market is ripe and act benefits greatly. However, acting if the market is not ripe generates large losses, and checking market conditions is costly. Other natural contexts in which competition for opportunities occurs include innovation, patenting, launching new products, selling assets, head hunting, and dating. What is common to all these examples is that the right moment to act depends on identifying an unobservable event, while taking the other player's strategy into account. Ran Eilat, Zvika Neeman, Eilon Solan |
EC | 1 |