Selective Data Acquisition in the Wild for Model Charging
Summary: AutoData enables end-to-end selective labeled-data acquisition from heterogeneous real-world sources for model charging. It first discovers relevant datasets; then cross-source data are clustered, and a bandit/DRL-driven sampler iteratively selects clusters, samples points, and updates rewards to optimize utility. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Chengliang Chai (Tsinghua University)
- 2. Jiabin Liu (Tsinghua University)
- 3. Nan Tang (Qatar Computing Research Institute)
- 4. Guoliang Li (Tsinghua University)
- 5. Yuyu Luo (Tsinghua University)
BibTeX Citation
@article{chai_vldb22,
title = {{Selective Data Acquisition in the Wild for Model Charging}},
author = {Chai, Chengliang and Liu, Jiabin and Tang, Nan and Li, Guoliang and Luo, Yuyu},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {7},
pages = {1466--1478},
doi = {10.14778/3523210.3523223},
url = {https://doi.org/10.14778/3523210.3523223},
year = {2022}
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