Exploratory Training: When Annotators Learn About Data
Summary: Game-theoretic collaborative active learning where annotator beliefs evolve with data as users revise strategies. Theory and algorithms for joint user-system convergence to a shared target model, reducing interactions, validated on real-world studies. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Rajesh Shrestha (Oregon State University)
- 2. Omeed Habibelahian (Oregon State University)
- 3. Arash Termehchy (Oregon State University)
- 4. Paolo Papotti (EURECOM)
BibTeX Citation
@inproceedings{shrestha_sigmod23,
title = {{Exploratory Training: When Annotators Learn About Data}},
author = {Shrestha, Rajesh and Habibelahian, Omeed and Termehchy, Arash and Papotti, Paolo},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3589280},
url = {https://dl.acm.org/doi/10.1145/3589280},
year = {2023}
}
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,945 | Generation of Training Examples for Tabular Natural Language Inference | 2023 | SIGMOD | 5.3480421e-05 |
| 10,681 | User-Centric Property Graph Repairs | 2025 | SIGMOD | 5.093636e-05 |
| 11,069 | Versatile Property Graph Transformations | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 21 of 21 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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