Learning Generalizable Batch Active Learning Strategies via Deep Q-Networks (Student Abstract)

Yi-Chen Li, Wen-Jie Shen, Boyu Zhang, Feng Mao, Zongzhang Zhang, Yang Yu

AAAI 2023 pp. 16258-16259

doi:10.1609/AAAI.V37I13.26989 /aaai/2023/li2023aaai-learning-f/

Abstract

To handle a large amount of unlabeled data, batch active learning (BAL) queries humans for the labels of a batch of the most valuable data points at every round. Most current BAL strategies are based on human-designed heuristics, such as uncertainty sampling or mutual information maximization. However, there exists a disagreement between these heuristics and the ultimate goal of BAL, i.e., optimizing the model's final performance within the query budgets. This disagreement leads to a limited generality of these heuristics. To this end, we formulate BAL as an MDP and propose a data-driven approach based on deep reinforcement learning. Our method learns the BAL strategy by maximizing the model's final performance. Experiments on the UCI benchmark show that our method can achieve competitive performance compared to existing heuristics-based approaches.

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Cite

Text

Li et al. "Learning Generalizable Batch Active Learning Strategies via Deep Q-Networks (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I13.26989

Markdown

[Li et al. "Learning Generalizable Batch Active Learning Strategies via Deep Q-Networks (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/li2023aaai-learning-f/) doi:10.1609/AAAI.V37I13.26989

BibTeX

@inproceedings{li2023aaai-learning-f,
  title     = {{Learning Generalizable Batch Active Learning Strategies via Deep Q-Networks (Student Abstract)}},
  author    = {Li, Yi-Chen and Shen, Wen-Jie and Zhang, Boyu and Mao, Feng and Zhang, Zongzhang and Yu, Yang},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2023},
  pages     = {16258-16259},
  doi       = {10.1609/AAAI.V37I13.26989},
  url       = {https://mlanthology.org/aaai/2023/li2023aaai-learning-f/}
}