Demo Data from EDBT 2026 / reviewers in the wild / expert
Marco Milanta
dblp:337/9952
· DBLP profile ↗
1ranked-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 · 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 |
Learning theory · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Learning theory
inductive bias |
0.7 | 1 | 2023 | Strong inductive biases provably prevent harmless interpolation · ICLR 2023 |
Machine learning › Learning theory › over-parameterization
interpolation |
0.7 | 1 | 2023 | Strong inductive biases provably prevent harmless interpolation · ICLR 2023 |
Methods — techniques the papers use, named apart from their topics
generalization theory · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Strong inductive biases provably prevent harmless interpolation
Michael Aerni, Marco Milanta, Konstantin Donhauser, Fanny Yang |
ICLR | 2 |