Demo Data from VLDB 2026 / reviewers in the wild / expert
Luciano Pomatto
dblp:275/6505
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · unresolved
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% | |
| Artificial intelligence
1 paper |
Reinforcement learning · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Algorithmic game theory and mechanism design
decision theory |
0.6 | 1 | 2022 | Monotone Additive Statistics · EC 2022 |
Machine learning › Reinforcement learning
stochastic dominance |
0.2 | 1 | 2022 | Monotone Additive Statistics · EC 2022 |
Methods — techniques the papers use, named apart from their topics
axiomatic characterization · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Monotone Additive StatisticsabstractThe expectation is an example of a descriptive statistic that is monotone with respect to stochastic dominance, and additive for sums of independent random variables. We provide a complete characterization of such statistics, and explore a number of applications to models of individual and group decision-making. These include a representation of stationary, monotone time preferences, extending the work of Fishburn and Rubinstein (1982) to time lotteries, as well as a characterization of risk-averse preferences over monetary gambles that are invariant to mean-zero background risks. Xiaosheng Mu, Luciano Pomatto, Philipp Strack, Omer Tamuz |
EC | 2 |