Demo Data from SIGIR 2026 / reviewers in the wild / expert
Nathaniel Helgesen
dblp:306/5329
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 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 · 62% Learning paradigms · 38% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Trustworthy machine learning
calibration |
0.8 | 1 | 2024 | Flexible Distribution Alignment: Towards Long-Tailed Semi-supervised Learning with Proper Calibration · ECCV (54) 2024 |
Machine learning › Learning paradigms › semi-supervised learning
long-tailed semi-supervised learning |
0.2 | 1 | 2024 | Flexible Distribution Alignment: Towards Long-Tailed Semi-supervised Learning with Proper Calibration · ECCV (54) 2024 |
Machine learning › Learning paradigms
semi-supervised learning |
0.2 | 1 | 2024 | Flexible Distribution Alignment: Towards Long-Tailed Semi-supervised Learning with Proper Calibration · ECCV (54) 2024 |
Methods — techniques the papers use, named apart from their topics
distribution alignment · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Flexible Distribution Alignment: Towards Long-Tailed Semi-supervised Learning with Proper Calibration
Emanuel Sanchez Aimar, Nathaniel Helgesen, Yonghao Xu, Marco Kuhlmann, Michael Felsberg |
ECCV (54) | 2 |