Demo Data from EDBT 2026 / reviewers in the wild / expert
Federico Sandrelli
dblp:328/5208
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—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 · 100% |
Topics — the 1 heaviest of 1, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Reinforcement learning › safe reinforcement learning › risk-sensitive reinforcement learning
risk-averse policy gradient |
0.6 | 1 | 2022 | Risk-averse policy optimization via risk-neutral policy optimization · Artif. Intell. 2022 |
Methods — techniques the papers use, named apart from their topics
risk-neutral policy optimization · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Risk-averse policy optimization via risk-neutral policy optimization
Lorenzo Bisi, Davide Santambrogio, Federico Sandrelli, Andrea Tirinzoni, Brian D. Ziebart, Marcello Restelli |
Artif. Intell. | 3 |