Demo Data from EDBT 2026 / reviewers in the wild / expert
Soumyajit De
dblp:190/7836
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1
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 · 50% Generative modeling · 25% Probabilistic and Bayesian machine learning · 25% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Generative modeling
generative model evaluation |
0.3 | 1 | 2017 | Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy · ICLR (Poster) 2017 |
Machine learning › Learning theory
hypothesis testing |
0.3 | 1 | 2017 | Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy · ICLR (Poster) 2017 |
Machine learning › Learning theory › probability metric › integral probability metric
maximum mean discrepancy |
0.3 | 1 | 2017 | Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy · ICLR (Poster) 2017 |
Machine learning › Probabilistic and Bayesian machine learning
model criticism |
0.3 | 1 | 2017 | Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy · ICLR (Poster) 2017 |
Methods — techniques the papers use, named apart from their topics
maximum mean discrepancy · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy
Danica J. Sutherland, Hsiao-Yu Fish Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alexander J. Smola, Arthur Gretton |
ICLR (Poster) | 4 |