Demo Data from EDBT 2026 / reviewers in the wild / expert
Artem Beliakov
dblp:322/3798
· 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 |
Probabilistic and Bayesian machine learning · 44% Kernel, tree and ensemble methods · 44% Reinforcement learning · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Probabilistic and Bayesian machine learning › stochastic processes
gaussian process |
0.7 | 1 | 2023 | Gradient Boosting Performs Gaussian Process Inference · ICLR 2023 |
Machine learning › Kernel, tree and ensemble methods
gradient boosting |
0.7 | 1 | 2023 | Gradient Boosting Performs Gaussian Process Inference · ICLR 2023 |
Machine learning › Reinforcement learning
function approximation |
0.2 | 1 | 2023 | Gradient Boosting Performs Gaussian Process Inference · ICLR 2023 |
Methods — techniques the papers use, named apart from their topics
gradient boosting · 0.7gaussian process inference · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Gradient Boosting Performs Gaussian Process Inference
Aleksei Ustimenko, Artem Beliakov, Liudmila Ostroumova |
ICLR | 2 |