EDBT 2026 Demo / reviewers in the wild / expert
Joshua D. Amason
dblp:296/4201 · also Joshua David Amason
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—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 · 50% Optimization for machine learning · 50% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Optimization for machine learning
gradient-based learning |
0.6 | 1 | 2022 | Gradient Importance Learning for Incomplete Observations · ICLR 2022 |
Machine learning › Trustworthy machine learning
learning with incomplete data |
0.6 | 1 | 2022 | Gradient Importance Learning for Incomplete Observations · ICLR 2022 |
Methods — techniques the papers use, named apart from their topics
gradient importance weighting · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Gradient Importance Learning for Incomplete Observations
Qitong Gao, Dong Wang 0037, Joshua D. Amason, Siyang Yuan, Chenyang Tao, Ricardo Henao, Majda Hadziahmetovic, Lawrence Carin, Miroslav Pajic |
ICLR | 3 |