VLDB 2026 Research / reviewers in the wild / expert
Becks Simpson
dblp:239/5125
· 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 |
Trustworthy machine learning · 75% Learning theory · 25% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Learning theory
generalization |
0.5 | 1 | 2021 | Saliency is a Possible Red Herring When Diagnosing Poor Generalization · ICLR 2021 |
Machine learning › Trustworthy machine learning
interpretability |
0.5 | 1 | 2021 | Saliency is a Possible Red Herring When Diagnosing Poor Generalization · ICLR 2021 |
Machine learning › Trustworthy machine learning
robustness |
0.5 | 1 | 2021 | Saliency is a Possible Red Herring When Diagnosing Poor Generalization · ICLR 2021 |
Machine learning › Trustworthy machine learning › interpretability › attribution methods
saliency methods |
0.5 | 1 | 2021 | Saliency is a Possible Red Herring When Diagnosing Poor Generalization · ICLR 2021 |
Methods — techniques the papers use, named apart from their topics
saliency analysis · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Saliency is a Possible Red Herring When Diagnosing Poor Generalization
Joseph D. Viviano, Becks Simpson, Francis Dutil, Yoshua Bengio, Joseph Paul Cohen |
ICLR | 2 |