thesis/bibliography/thesis.bib

@article{tong2016neyman,
  title={A survey on {Neyman--Pearson} classification and suggestions for future research},
  author={Tong, Xin and Feng, Yang and Zhao, Anqi},
  journal={{WIREs} Computational Statistics},
  volume={8},
  pages={64--81},
  year={2016},
  doi={10.1002/wics.1376},
  publisher={Wiley}
}

@book{silverman1986density,
  title={Density Estimation for Statistics and Data Analysis},
  author={Silverman, B. W.},
  year={1986},
  publisher={Chapman and Hall},
  address={London},
  series={Chapman \& Hall/CRC Monographs on Statistics and Applied Probability}
}

@article{botev2010kde,
  title={Kernel density estimation via diffusion},
  author={Botev, Z. I. and Grotowski, J. F. and Kroese, D. P.},
  journal={The Annals of Statistics},
  volume={38},
  number={5},
  pages={2916--2957},
  year={2010},
  doi={10.1214/10-aos799},
  publisher={Institute of Mathematical Statistics}
}

@book{press2007numerical,
  title={Numerical Recipes: The Art of Scientific Computing},
  author={Press, William H. and Teukolsky, Saul A. and Vetterling, William T. and Flannery, Brian P.},
  edition={3rd},
  year={2007},
  publisher={Cambridge University Press},
  address={Cambridge},
  isbn={978-0-521-88068-8}
}

@article{chen2022beta,
  title={Novel closed-form point estimators for the beta distribution},
  author={Chen, Piao and Xiao, Xun},
  journal={arXiv preprint arXiv:2210.05536},
  year={2022}
}

@article{demaine2007jigsaw,
  title={Jigsaw puzzles, edge matching, and polyomino packing: Connections and complexity},
  author={Demaine, Erik D. and Demaine, Martin L.},
  journal={Graphs and Combinatorics},
  volume={23},
  number={1},
  pages={195--208},
  year={2007},
  publisher={Springer}
}

@article{xie2024wsauc,
  title={Weakly Supervised {AUC} Optimization: A Unified Partial {AUC} Approach},
  author={Xie, Zheng and Liu, Yu and He, Hao-Yuan and Li, Ming and Zhou, Zhi-Hua},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2024},
  doi={10.1109/TPAMI.2024.3357814},
  eprint={2305.14258},
  archivePrefix={arXiv}
}

@inproceedings{wu2025preserving,
  title={Preserving {AUC} Fairness in Learning with Noisy Protected Groups},
  author={Wu, Mingyang and Lin, Li and Zhang, Wenbin and Wang, Xin and Yang, Zhenhuan and Hu, Shu},
  booktitle={Proceedings of the 42nd International Conference on Machine Learning},
  year={2025},
  eprint={2505.18532},
  archivePrefix={arXiv}
}

@article{altman1989solving,
  title={Solving the jigsaw puzzle problem in linear time},
  author={Altman, Tom},
  journal={Applied Artificial Intelligence an International Journal},
  volume={3},
  number={4},
  pages={453--462},
  year={1989},
  publisher={Taylor \& Francis}
}

@inproceedings{ghosh2017robust,
  title={Robust Loss Functions under Label Noise for Deep Neural Networks},
  author={Ghosh, Aritra and Kumar, Himanshu and Sastry, P. S.},
  booktitle={Proceedings of the Thirty-First {AAAI} Conference on Artificial Intelligence},
  pages={1919--1925},
  year={2017}
}

@inproceedings{arpit2017memorization,
  title={A Closer Look at Memorization in Deep Networks},
  author={Arpit, Devansh and Jastrz\k{e}bski, Stanis{\l}aw and Ballas, Nicolas and Krueger, David and Bengio, Emmanuel and Kanwal, Maxinder S. and Maharaj, Tegan and Fischer, Asja and Courville, Aaron and Bengio, Yoshua and Lacoste-Julien, Simon},
  booktitle={Proceedings of the 34th International Conference on Machine Learning},
  pages={233--242},
  year={2017}
}

@inproceedings{zhang2018generalized,
  title={Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels},
  author={Zhang, Zhilu and Sabuncu, Mert},
  booktitle={Advances in Neural Information Processing Systems},
  volume={31},
  year={2018}
}

@inproceedings{patrini2017making,
  title={Making Deep Neural Networks Robust to Label Noise: {A} Loss Correction Approach},
  author={Patrini, Giorgio and Rozza, Alessandro and Krishna Menon, Aditya and Nock, Richard and Qu, Lizhen},
  booktitle={Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition},
  pages={1944--1952},
  year={2017}
}

@inproceedings{han2018coteaching,
  title={Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels},
  author={Han, Bo and Yao, Quanming and Yu, Xingrui and Niu, Gang and Xu, Miao and Hu, Weihua and Tsang, Ivor and Sugiyama, Masashi},
  booktitle={Advances in Neural Information Processing Systems},
  volume={31},
  year={2018}
}

@inproceedings{li2020dividemix,
  title={{DivideMix}: Learning with Noisy Labels as Semi-Supervised Learning},
  author={Li, Junnan and Socher, Richard and Hoi, Steven C. H.},
  booktitle={International Conference on Learning Representations},
  year={2020}
}

@inproceedings{ren2018learning,
  title={Learning to Reweight Examples for Robust Deep Learning},
  author={Ren, Mengye and Zeng, Wenyuan and Yang, Bin and Urtasun, Raquel},
  booktitle={Proceedings of the 35th International Conference on Machine Learning},
  pages={4334--4343},
  year={2018}
}

@inproceedings{feng2020can,
  title={Can Cross Entropy Loss Be Robust to Label Noise?},
  author={Feng, Lei and Shu, Senlin and Lin, Zhuoyi and Lv, Fengmei and Li, Li and An, Bo},
  booktitle={Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence},
  pages={2206--2212},
  year={2020}
}

@inproceedings{zhou2021asymmetric,
  title={Asymmetric Loss Functions for Learning with Noisy Labels},
  author={Zhou, Xiong and Liu, Xianming and Jiang, Junjun and Gao, Xin and Ji, Xiangyang},
  booktitle={Proceedings of the 38th International Conference on Machine Learning},
  pages={12846--12856},
  year={2021}
}