Demo Data from EDBT 2026 / reviewers in the wild / expert
Philippe Bonnard
dblp:49/6906
· DBLP profile ↗
2ranked-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 · 2 · 1 since 2021Databases, data management, data science and information retrieval · 1
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 |
Probabilistic and Bayesian machine learning · 50% Knowledge representation and reasoning · 50% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Knowledge representation and reasoning
rule learning |
0.7 | 1 | 2023 | Neural-based classification rule learning for sequential data · ICLR 2023 |
Machine learning › Probabilistic and Bayesian machine learning › structured prediction
sequential classification |
0.7 | 1 | 2023 | Neural-based classification rule learning for sequential data · ICLR 2023 |
Methods — techniques the papers use, named apart from their topics
neural network · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Neural-based classification rule learning for sequential data
Marine Collery, Philippe Bonnard, François Fages, Remy Kusters |
ICLR | 2 |
| 2016 | Incremental Junction Tree Inference
Hamza Agli, Philippe Bonnard, Christophe Gonzales, Pierre-Henri Wuillemin |
IPMU (1) | 2 |