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)
Incoming Non-self Citations Over Time
Authors
- 1. Huayi Zhang (Worcester Polytechnic Institute)
- 2. Lei Cao (Massachusetts Institute of Technology)
- 3. Samuel Madden (Massachusetts Institute of Technology)
- 4. Elke Rundensteiner (Worcester Polytechnic Institute)
BibTeX Citation
@article{zhang_vldb21,
title = {{LANCET: Labeling Complex Data at Scale}},
author = {Zhang, Huayi and Cao, Lei and Madden, Samuel and Rundensteiner, Elke},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {11},
pages = {2154--2166},
doi = {10.14778/3476249.3476269},
url = {https://doi.org/10.14778/3476249.3476269},
year = {2021}
}
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,108 | VOCALExplore: Pay-as-You-Go Video Data Exploration and Model Building | 2023 | VLDB | 5.0789354e-05 |
| 11,101 | Agree to Disagree: Robust Anomaly Detection with Noisy Labels | 2025 | SIGMOD | 4.9793485e-05 |
| 11,556 | MetaStore: Analyzing Deep Learning Meta-Data at Scale | 2024 | VLDB | 4.9793485e-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.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 205 | Snorkel: Rapid Training Data Creation with Weak Supervision | 2018 | VLDB | 0.00025181304 |
| 1,120 | Snuba: Automating Weak Supervision to Label Training Data | 2019 | VLDB | 0.00011946047 |
| 3,955 | Smile: A System to Support Machine Learning on EEG Data at Scale | 2019 | VLDB | 6.9025583e-05 |
| 4,543 | GOGGLES: Automatic Image Labeling with Affinity Coding | 2020 | SIGMOD | 6.5470116e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 12,198 | Entity Matching with Active Monotone Classification | 2018 | PODS |
| 2 | 11,767 | Self-Training for Label-Efficient Information Extraction from Semi-Structured Web-Pages | 2023 | VLDB |
| 3 | 6,937 | Inspector Gadget: A Data Programming-based Labeling System for Industrial Images | 2021 | VLDB |
| 4 | 205 | Snorkel: Rapid Training Data Creation with Weak Supervision | 2018 | VLDB |
| 5 | 1,120 | Snuba: Automating Weak Supervision to Label Training Data | 2019 | VLDB |
| 6 | 8,664 | CrowdGame: A Game-Based Crowdsourcing System for Cost-Effective Data Labeling | 2019 | SIGMOD |
| 7 | 7,648 | MisDetect: Iterative Mislabel Detection using Early Loss | 2024 | VLDB |
| 8 | 10,211 | CORAL: Collaborative Automatic Labeling System based on Large Language Models | 2024 | VLDB |
| 9 | 2,642 | Scaling Up Crowd-Sourcing to Very Large Datasets: A Case for Active Learning | 2015 | VLDB |
| 10 | 4,543 | GOGGLES: Automatic Image Labeling with Affinity Coding | 2020 | SIGMOD |