EDBT 2026 Demo / reviewers in the wild / expert
Rafael Muller
dblp:277/0604
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
—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 |
Kernel, tree and ensemble methods · 61% Trustworthy machine learning · 30% Deep learning architectures and training · 9% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Trustworthy machine learning
calibration |
0.5 | 1 | 2021 | Combining Ensembles and Data Augmentation Can Harm Your Calibration · ICLR 2021 |
Machine learning › Kernel, tree and ensemble methods › ensemble learning
ensemble calibration |
0.5 | 1 | 2021 | Combining Ensembles and Data Augmentation Can Harm Your Calibration · ICLR 2021 |
Machine learning › Kernel, tree and ensemble methods
model ensemble |
0.5 | 1 | 2021 | Combining Ensembles and Data Augmentation Can Harm Your Calibration · ICLR 2021 |
Machine learning › Deep learning architectures and training
data augmentation |
0.1 | 1 | 2021 | Combining Ensembles and Data Augmentation Can Harm Your Calibration · ICLR 2021 |
Methods — techniques the papers use, named apart from their topics
ensembling · 0.5data augmentation · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Combining Ensembles and Data Augmentation Can Harm Your Calibration
Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, Dustin Tran |
ICLR | 3 |