VLDB 2026 Research / reviewers in the wild / expert
Gabriel Prenassi
dblp:411/4337
· DBLP profile ↗
3ranked-venue papers
1as first author
3since 2021 · last 2026
0009-0006-8748-6745ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 since 2021Human-computer interaction and ubiquitous computing · 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
2 papers |
Trustworthy machine learning · 100% | |
| Databases, data mining, and information retrieval
2 papers |
Data mining · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data mining › text mining
text classification |
1.9 | 2 | 2026 | When High Accuracy Hides Poor Calibration: Rethinking Confidence Evaluation in Transformer-Based Text Classification with Balanced Brier Score · ACL (1) 2026 Instance-Selection-Inspired Undersampling Strategies for Bias Reduction in Small and Large Language Models for Binary Text Classification · ACL (1) 2025 |
Machine learning › Trustworthy machine learning
brier score |
1.0 | 1 | 2026 | When High Accuracy Hides Poor Calibration: Rethinking Confidence Evaluation in Transformer-Based Text Classification with Balanced Brier Score · ACL (1) 2026 |
Machine learning › Trustworthy machine learning › calibration
confidence calibration |
1.0 | 1 | 2026 | When High Accuracy Hides Poor Calibration: Rethinking Confidence Evaluation in Transformer-Based Text Classification with Balanced Brier Score · ACL (1) 2026 |
Machine learning › Trustworthy machine learning
uncertainty and calibration |
1.0 | 1 | 2026 | When High Accuracy Hides Poor Calibration: Rethinking Confidence Evaluation in Transformer-Based Text Classification with Balanced Brier Score · ACL (1) 2026 |
Machine learning › Trustworthy machine learning › fairness
bias mitigation |
0.9 | 1 | 2025 | Instance-Selection-Inspired Undersampling Strategies for Bias Reduction in Small and Large Language Models for Binary Text Classification · ACL (1) 2025 |
Methods — techniques the papers use, named apart from their topics
brier score · 2.0instance selection · 1.7undersampling · 0.9under-sampling · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | When High Accuracy Hides Poor Calibration: Rethinking Confidence Evaluation in Transformer-Based Text Classification with Balanced Brier ScoreabstractGuilherme Fonseca, Gabriel Prenassi, Washington Cunha, Leonardo Chaves Dutra da Rocha, Marcos André Gonçalves. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026. Guilherme Fonseca, Gabriel Prenassi, Washington Cunha, Leonardo Rocha 0001, Marcos André Gonçalves |
ACL (1) | 2 |
| 2025 | Instance-Selection-Inspired Undersampling Strategies for Bias Reduction in Small and Large Language Models for Binary Text ClassificationabstractGuilherme Fonseca, Washington Cunha, Gabriel Prenassi, Marcos André Gonçalves, Leonardo Chaves Dutra Da Rocha. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025. Guilherme Fonseca, Washington Cunha, Gabriel Prenassi, Marcos André Gonçalves, Leonardo Rocha 0001 |
ACL (1) | 3 |
| 2025 | Enhanced Analysis of User Perceptions Through Natural Language Processing Approaches
Gabriel Prenassi, Ana Cláudia Machado, Davi Freitas, André Lima, Raquel Oliveira Prates, Antonio Landim, Leonardo Rocha 0001, Elisa Tuler de Albergaria |
INTERACT (3) | 1 |