Database Paper Browser

Back to papers

LANCET: Labeling Complex Data at Scale

Summary: Unifies auto-labeling tasks: what, how, when. Guided by Covariate-shift and Continuity, LANCET maps data to semantic space, keeps labeled neighbors, and uses a distribution-matching network to decide when labeling is safe; outperforms Snuba/GOGGLES by 30pp. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12394
Venue
VLDB
Year
2021
Pagerank
4.4619818e-05
Overall Rank
8,714 | 39.38%
DOI
10.14778/3476249.3476269

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
9,769 VOCALExplore: Pay-as-You-Go Video Data Exploration and Model Building 2023 VLDB 4.2856106e-05
10,365 Agree to Disagree: Robust Anomaly Detection with Noisy Labels 2025 SIGMOD 4.1945683e-05
11,008 MetaStore: Analyzing Deep Learning Meta-Data at Scale 2024 VLDB 4.1945683e-05
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Previous Page 1 / 1 Next

Semantically Similar Papers