This repository is a supplement to the dissertation report "Contextual Bandits Active Learning: An Ensemble Perspective of Active Learning"
Codes from this repository are adapted from 3 other repositories:
To cite this work, use the following:
@mastersthesis{song2020cbal,
title={Contextual Bandits Active Learning: An Ensemble Perspective of Active Learning},
author={Song, Wan Jing},
school={University College London},
year=2020
}
1:
@techreport{YY2017,
author = {Yao-Yuan Yang and Shao-Chuan Lee and Yu-An Chung and Tung-En Wu and Si-An Chen and Hsuan-Tien Lin},
title = {libact: Pool-based Active Learning in Python},
institution = {National Taiwan University},
url = {https://github.com/ntucllab/libact},
note = {available as arXiv preprint \url{https://arxiv.org/abs/1710.00379}},
month = oct,
year = 2017
}
2:
@inproceedings{huang2010active,
title={Active learning by querying informative and representative examples},
author={Huang, Sheng-Jun and Jin, Rong and Zhou, Zhi-Hua},
booktitle={Advances in neural information processing systems},
pages={892--900},
year={2010}
}
3:
@article{riquelme2018deep,
title={Deep Bayesian Bandits Showdown: An Empirical
Comparison of Bayesian Deep Networks for Thompson Sampling},
author={Riquelme, Carlos and Tucker, George and Snoek, Jasper},
journal={International Conference on Learning Representations, ICLR.}, year={2018}
}
4:
@misc{huang2018,
author = {Kuan-Hao Huang},
title = {Deep Active Learning},
year = {2018},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/ej0cl6/deep-active-learning}},
}