VLDB 2026 Research / reviewers in the wild / expert
Jonas Vlasselaer
dblp:150/5902
· DBLP profile ↗
9ranked-venue papers
5as first author
0since 2021 · last 2018
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 9 · 5 first-authorDatabases, data management, data science and information retrieval · 2 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 2 · 2 first-authorTheory of computation · 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
2 papers |
Probabilistic and Bayesian machine learning · 42% Optimization for machine learning · 21% Knowledge representation and reasoning · 18% | |
| Theoretical computer science
1 paper |
Automated reasoning and model checking · 100% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Probabilistic and Bayesian machine learning › structured models › graphical models › bayesian network
dynamic bayesian network |
0.2 | 1 | 2016 | Exploiting local and repeated structure in Dynamic Bayesian Networks · Artif. Intell. 2016 |
Machine learning › Probabilistic and Bayesian machine learning › structured models
graphical models |
0.2 | 1 | 2016 | Exploiting local and repeated structure in Dynamic Bayesian Networks · Artif. Intell. 2016 |
Machine learning › Optimization for machine learning
structure exploitation |
0.2 | 1 | 2016 | Exploiting local and repeated structure in Dynamic Bayesian Networks · Artif. Intell. 2016 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
anytime algorithm |
0.2 | 1 | 2015 | Anytime Inference in Probabilistic Logic Programs with Tp-Compilation · IJCAI 2015 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › logic programming
probabilistic logic programming |
0.2 | 1 | 2015 | Anytime Inference in Probabilistic Logic Programs with Tp-Compilation · IJCAI 2015 |
Automated reasoning and model checking
probabilistic inference |
0.2 | 1 | 2015 | Anytime Inference in Probabilistic Logic Programs with Tp-Compilation · IJCAI 2015 |
Methods — techniques the papers use, named apart from their topics
tp-compilation · 0.4anytime inference · 0.4repeated structure · 0.2local structure · 0.2dynamic bayesian network · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | Feature noise tuning for resource efficient Bayesian Network Classifiers
Laura Isabel Galindez Olascoaga, Jonas Vlasselaer, Wannes Meert, Marian Verhelst |
ESANN | 2 |
| 2018 | Towards Resource-Efficient Classifiers for Always-On Monitoring
Jonas Vlasselaer, Wannes Meert, Marian Verhelst |
ECML/PKDD (3) | 1 |
| 2016 | Exploiting local and repeated structure in Dynamic Bayesian Networks
Jonas Vlasselaer, Wannes Meert, Guy Van den Broeck, Luc De Raedt |
Artif. Intell. | 1 |
| 2016 | TP-Compilation for inference in probabilistic logic programs
Jonas Vlasselaer, Guy Van den Broeck, Angelika Kimmig, Wannes Meert, Luc De Raedt |
Int. J. Approx. Reason. | 1 |
| 2015 | Anytime Inference in Probabilistic Logic Programs with Tp-Compilation
Jonas Vlasselaer, Guy Van den Broeck, Angelika Kimmig, Wannes Meert, Luc De Raedt |
IJCAI | 1 |
| 2015 | ProbLog2: Probabilistic Logic Programming
Anton Dries, Angelika Kimmig, Wannes Meert, Joris Renkens, Guy Van den Broeck, Jonas Vlasselaer, Luc De Raedt |
ECML/PKDD (3) | 6 |
| 2015 | LS-SVM based spectral clustering and regression for predicting maintenance of industrial machines
Rocco Langone, Carlos Alzate, Bart De Ketelaere, Jonas Vlasselaer, Wannes Meert, Johan A. K. Suykens |
Eng. Appl. Artif. Intell. | 4 |
| 2014 | Condition Monitoring with Incomplete ObservationsabstractWe introduce an approach for predicting the behaviour of a machine during a production cycle. Typical data analysis methods assume that continuous behaviour is (fully) observed. This assumption is unrealistic as monitored machines are often interrupted and restarted at irregular points in time. We study the resulting problem, propose a solution and report on a use-case in wire drawing. Jonas Vlasselaer, Wannes Meert, Rocco Langone, Luc De Raedt |
ECAI | 1 |
| 2014 | The Most Probable Explanation for Probabilistic Logic Programs with Annotated Disjunctions
Dimitar Sht. Shterionov, Joris Renkens, Jonas Vlasselaer, Angelika Kimmig, Wannes Meert, Gerda Janssens |
ILP | 3 |