EDBT 2026 Demo / reviewers in the wild / expert
Prakruthi Pradeep
dblp:324/4040
· 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.
| Databases, data mining, and information retrieval
1 paper |
Machine learning and data management · 67% Data mining · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data mining
crowdsourcing |
0.7 | 1 | 2023 | Ordinal Programmatic Weak Supervision and Crowdsourcing for Estimating Cognitive States (Student Abstract) · AAAI 2023 |
Machine learning and data management › weak supervision
programmatic weak supervision |
0.7 | 1 | 2023 | Ordinal Programmatic Weak Supervision and Crowdsourcing for Estimating Cognitive States (Student Abstract) · AAAI 2023 |
Machine learning and data management
weak supervision |
0.7 | 1 | 2023 | Ordinal Programmatic Weak Supervision and Crowdsourcing for Estimating Cognitive States (Student Abstract) · AAAI 2023 |
Methods — techniques the papers use, named apart from their topics
factor graph · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Ordinal Programmatic Weak Supervision and Crowdsourcing for Estimating Cognitive States (Student Abstract)abstractCrowdsourcing and weak supervision offer methods to efficiently label large datasets. Our work builds on existing weak supervision models to accommodate ordinal target classes, in an effort to recover ground truth from weak, external labels. We define a parameterized factor function and show that our approach improves over other baselines. Prakruthi Pradeep, Benedikt Boecking, Nicholas Gisolfi, Jacob R. Kintz, Torin K. Clark, Artur Dubrawski |
AAAI | 1 |