Demo Data from EDBT 2026 / reviewers in the wild / expert
Taemin Park
dblp:119/7177
· DBLP profile ↗
5ranked-venue papers
3as first author
2since 2021 · last 2025
0009-0008-7235-3231ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Security and privacy · 3 · 2 first-authorArtificial intelligence and machine learning · 2 · 1 first-author · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-author · 2 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
2 papers |
Learning paradigms · 84% Trustworthy machine learning · 16% | |
| Network and information security
1 paper |
Web and mobile security · 100% |
Topics — the 6 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Learning paradigms
semi-supervised learning |
1.6 | 2 | 2025 | Learnable Logit Adjustment for Imbalanced Semi-Supervised Learning Under Class Distribution Mismatch · ICCV 2025 Rebalancing Using Estimated Class Distribution for Imbalanced Semi-supervised Learning Under Class Distribution Mismatch · ECCV (22) 2024 |
Machine learning › Learning paradigms › semi-supervised learning
class distribution mismatch |
0.9 | 1 | 2025 | Learnable Logit Adjustment for Imbalanced Semi-Supervised Learning Under Class Distribution Mismatch · ICCV 2025 |
Machine learning › Learning paradigms › semi-supervised learning › class distribution mismatch
imbalanced semi-supervised learning |
0.9 | 1 | 2025 | Learnable Logit Adjustment for Imbalanced Semi-Supervised Learning Under Class Distribution Mismatch · ICCV 2025 |
Machine learning › Learning paradigms
class imbalance |
0.8 | 1 | 2024 | Rebalancing Using Estimated Class Distribution for Imbalanced Semi-supervised Learning Under Class Distribution Mismatch · ECCV (22) 2024 |
Machine learning › Trustworthy machine learning
fairness |
0.8 | 1 | 2024 | Rebalancing Using Estimated Class Distribution for Imbalanced Semi-supervised Learning Under Class Distribution Mismatch · ECCV (22) 2024 |
Web and mobile security › web security
javascript engine security |
0.4 | 1 | 2020 | NoJITsu: Locking Down JavaScript Engines · NDSS 2020 |
Methods — techniques the papers use, named apart from their topics
logit adjustment · 0.9JIT hardening · 0.9re-balancing · 0.8class distribution estimation · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Learnable Logit Adjustment for Imbalanced Semi-Supervised Learning Under Class Distribution Mismatch
Hyuck Lee, Taemin Park, Heeyoung Kim |
ICCV | 2 |
| 2024 | Rebalancing Using Estimated Class Distribution for Imbalanced Semi-supervised Learning Under Class Distribution Mismatch
Taemin Park, Hyuck Lee, Heeyoung Kim |
ECCV (22) | 1 |
| 2020 | NoJITsu: Locking Down JavaScript Engines
Taemin Park, Karel Dhondt, David Gens, Yeoul Na, Stijn Volckaert, Michael Franz |
NDSS | 1 |
| 2018 | Bytecode Corruption Attacks Are Real - And How to Defend Against Them
Taemin Park, Julian Lettner, Yeoul Na, Stijn Volckaert, Michael Franz |
DIMVA | 1 |
| 2018 | PartiSan: Fast and Flexible Sanitization via Run-Time Partitioning
Julian Lettner, Dokyung Song, Taemin Park, Per Larsen, Stijn Volckaert, Michael Franz |
RAID | 3 |