EDBT 2026 Demo / reviewers in the wild / expert
Robin Vaysse
dblp:281/6785
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 1 first-author · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 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 |
Learning theory · 33% Time series and sequential data · 33% Learning paradigms · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Learning paradigms
continual learning |
0.5 | 1 | 2021 | River: machine learning for streaming data in Python · J. Mach. Learn. Res. 2021 |
Machine learning › Learning theory
online learning |
0.5 | 1 | 2021 | River: machine learning for streaming data in Python · J. Mach. Learn. Res. 2021 |
Machine learning › Time series and sequential data
streaming data |
0.5 | 1 | 2021 | River: machine learning for streaming data in Python · J. Mach. Learn. Res. 2021 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Automatic Extraction of Speech Rhythm Descriptors for Speech Intelligibility Assessment in the Context of Head and Neck CancersabstractInternational audience Robin Vaysse, Jérôme Farinas, Corine Astésano, Régine André-Obrecht |
Interspeech | 1 |
| 2021 | River: machine learning for streaming data in PythonabstractRiver is a machine learning library for dynamic data streams and continual learning. It provides multiple state-of-the-art learning methods, data generators/transformers, performance metrics and evaluators for different stream learning problems. It is the result from the merger of two popular packages for stream learning in Python: Creme and scikit-multiflow. River introduces a revamped architecture based on the lessons learnt from the seminal packages. River's ambition is to be the go-to library for doing machine learning on streaming data. Additionally, this open source package brings under the same umbrella a large community of practitioners and researchers. The source code is available at https://github.com/online-ml/river. Jacob Montiel, Max Halford, Saulo Martiello Mastelini, Geoffrey Bolmier, Raphaël Sourty, Robin Vaysse, Adil Zouitine, Heitor Murilo Gomes, Jesse Read, Talel Abdessalem, Albert Bifet |
J. Mach. Learn. Res. | 6 |