VLDB 2026 Research / reviewers in the wild / expert
Luca La Rocca
dblp:11/8694
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
0000-0003-0495-1308ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Databases, data management, data science and information retrieval · 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 |
Trustworthy machine learning · 39% Knowledge representation and reasoning · 30% Efficient and distributed learning · 30% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Efficient and distributed learning
federated learning |
0.9 | 1 | 2025 | Mining Trustworthy Symbolic Regression Models in Federated Settings · ICDM 2025 |
Machine learning › Trustworthy machine learning
interpretability |
0.9 | 1 | 2025 | Mining Trustworthy Symbolic Regression Models in Federated Settings · ICDM 2025 |
Knowledge, reasoning and agents › Knowledge representation and reasoning
symbolic regression |
0.9 | 1 | 2025 | Mining Trustworthy Symbolic Regression Models in Federated Settings · ICDM 2025 |
Machine learning › Trustworthy machine learning
robustness |
0.3 | 1 | 2025 | Mining Trustworthy Symbolic Regression Models in Federated Settings · ICDM 2025 |
Methods — techniques the papers use, named apart from their topics
symbolic regression · 0.9federated learning · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Mining Trustworthy Symbolic Regression Models in Federated Settings
Mattia Billa, Veronica Guidetti, Luca La Rocca, Federica Mandreoli |
ICDM | 3 |