Demo Data from EDBT 2026 / reviewers in the wild / expert
Vedant Shah
dblp:304/8089
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—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.
| Artificial intelligence
1 paper |
Reinforcement learning · 56% Multi-agent systems · 44% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Multi-agent systems › multi-agent coordination
cooperative multi-agent coordination |
0.7 | 1 | 2023 | Stateful Active Facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning · ICLR 2023 |
Machine learning › Reinforcement learning
multi-agent reinforcement learning |
0.7 | 1 | 2023 | Stateful Active Facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning · ICLR 2023 |
Machine learning › Reinforcement learning
exploration |
0.2 | 1 | 2023 | Stateful Active Facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning · ICLR 2023 |
Methods — techniques the papers use, named apart from their topics
stateful active facilitator · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Stateful Active Facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning
Dianbo Liu, Vedant Shah, Oussama Boussif, Cristian Meo, Anirudh Goyal, Tianmin Shu, Michael C. Mozer, Nicolas Heess, Yoshua Bengio |
ICLR | 2 |