VLDB 2026 Research / reviewers in the wild / expert
Tzu-Ya Lai
dblp:338/7630
· DBLP profile ↗
1ranked-venue papers
1as 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 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 |
Graph learning · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational finance and economics · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Data mining · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Graph learning › graph neural network › attention-based graph neural network
graph attention network |
0.7 | 1 | 2023 | Sequential Graph Attention Learning for Predicting Dynamic Stock Trends (Student Abstract) · AAAI 2023 |
Computational finance and economics › financial market prediction › stock prediction
stock movement prediction |
0.7 | 1 | 2023 | Sequential Graph Attention Learning for Predicting Dynamic Stock Trends (Student Abstract) · AAAI 2023 |
Data mining › network analysis
temporal network analysis |
0.2 | 1 | 2023 | Sequential Graph Attention Learning for Predicting Dynamic Stock Trends (Student Abstract) · AAAI 2023 |
Methods — techniques the papers use, named apart from their topics
sequential modeling · 2.0graph attention · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Sequential Graph Attention Learning for Predicting Dynamic Stock Trends (Student Abstract)abstractThe stock market is characterized by a complex relationship between companies and the market. This study combines a sequential graph structure with attention mechanisms to learn global and local information within temporal time. Specifically, our proposed “GAT-AGNN” module compares model performance across multiple industries as well as within single industries. The results show that the proposed framework outperforms the state-of-the-art methods in predicting stock trends across multiple industries on Taiwan Stock datasets. Tzu-Ya Lai, Wen Jung Cheng, Jun-En Ding |
AAAI | 1 |