VLDB 2026 Research / reviewers in the wild / expert
Piotr Wyrwinski
dblp:275/0925
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 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 |
Knowledge representation and reasoning · 44% Planning, search and constraint satisfaction · 44% Optimization for machine learning · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
graph search |
0.9 | 1 | 2025 | Neuro-Guided Graph Search for Symbolic Regression (Student Abstract) · AAAI 2025 |
Knowledge, reasoning and agents › Knowledge representation and reasoning
symbolic regression |
0.9 | 1 | 2025 | Neuro-Guided Graph Search for Symbolic Regression (Student Abstract) · AAAI 2025 |
Machine learning › Optimization for machine learning
evolutionary computation |
0.3 | 1 | 2025 | Neuro-Guided Graph Search for Symbolic Regression (Student Abstract) · AAAI 2025 |
Methods — techniques the papers use, named apart from their topics
graph neural network · 0.9evolutionary algorithm · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Neuro-Guided Graph Search for Symbolic Regression (Student Abstract)abstractThis study introduces a neurosymbolic approach that performs iterative graph expansion guided by a graph neural network to solve symbolic regression problems. Empirical evaluation demonstrates superior performance of the method compared to baseline algorithms. We also integrate the method with an evolutionary algorithm, which results in further performance improvements. Piotr Wyrwinski, Krzysztof Krawiec |
AAAI | 1 |