319 lines
11 KiB
BibTeX
319 lines
11 KiB
BibTeX
@article{abramson1982bandwidth,
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title={On bandwidth variation in kernel estimates---a square root law},
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author={Abramson, Ian S.},
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journal={The Annals of Statistics},
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volume={10},
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number={4},
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pages={1217--1223},
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year={1982},
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publisher={Institute of Mathematical Statistics},
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doi={10.1214/aos/1176345986}
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}
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@article{terrell1992variable,
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title={Variable kernel density estimation},
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author={Terrell, George R. and Scott, David W.},
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journal={The Annals of Statistics},
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volume={20},
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number={3},
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pages={1236--1265},
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year={1992},
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publisher={Institute of Mathematical Statistics},
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doi={10.1214/aos/1176348768}
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}
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@article{tong2016neyman,
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title={A survey on {Neyman--Pearson} classification and suggestions for future research},
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author={Tong, Xin and Feng, Yang and Zhao, Anqi},
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journal={{WIREs} Computational Statistics},
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volume={8},
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pages={64--81},
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year={2016},
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doi={10.1002/wics.1376},
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publisher={Wiley}
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}
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@book{silverman1986density,
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title={Density Estimation for Statistics and Data Analysis},
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author={Silverman, B. W.},
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year={1986},
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publisher={Chapman and Hall},
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address={London},
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series={Chapman \& Hall/CRC Monographs on Statistics and Applied Probability}
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}
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@article{botev2010kde,
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title={Kernel density estimation via diffusion},
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author={Botev, Z. I. and Grotowski, J. F. and Kroese, D. P.},
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journal={The Annals of Statistics},
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volume={38},
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number={5},
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pages={2916--2957},
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year={2010},
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doi={10.1214/10-aos799},
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publisher={Institute of Mathematical Statistics}
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}
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@book{press2007numerical,
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title={Numerical Recipes: The Art of Scientific Computing},
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author={Press, William H. and Teukolsky, Saul A. and Vetterling, William T. and Flannery, Brian P.},
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edition={3rd},
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year={2007},
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publisher={Cambridge University Press},
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address={Cambridge},
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isbn={978-0-521-88068-8}
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}
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@article{chen2022beta,
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title={Novel closed-form point estimators for the beta distribution},
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author={Chen, Piao and Xiao, Xun},
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journal={arXiv preprint arXiv:2210.05536},
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year={2022}
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}
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@article{xie2024wsauc,
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title={Weakly Supervised {AUC} Optimization: A Unified Partial {AUC} Approach},
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author={Xie, Zheng and Liu, Yu and He, Hao-Yuan and Li, Ming and Zhou, Zhi-Hua},
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journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
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year={2024},
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doi={10.1109/TPAMI.2024.3357814},
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eprint={2305.14258},
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archivePrefix={arXiv}
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}
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@inproceedings{wu2025preserving,
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title={Preserving {AUC} Fairness in Learning with Noisy Protected Groups},
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author={Wu, Mingyang and Lin, Li and Zhang, Wenbin and Wang, Xin and Yang, Zhenhuan and Hu, Shu},
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booktitle={Proceedings of the 42nd International Conference on Machine Learning},
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year={2025},
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eprint={2505.18532},
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archivePrefix={arXiv}
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}
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@inproceedings{ghosh2017robust,
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title={Robust Loss Functions under Label Noise for Deep Neural Networks},
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author={Ghosh, Aritra and Kumar, Himanshu and Sastry, P. S.},
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booktitle={Proceedings of the Thirty-First {AAAI} Conference on Artificial Intelligence},
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pages={1919--1925},
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year={2017}
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}
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@inproceedings{arpit2017memorization,
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title={A Closer Look at Memorization in Deep Networks},
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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},
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booktitle={Proceedings of the 34th International Conference on Machine Learning},
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pages={233--242},
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year={2017}
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}
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@inproceedings{zhang2018generalized,
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title={Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels},
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author={Zhang, Zhilu and Sabuncu, Mert},
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booktitle={Advances in Neural Information Processing Systems},
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volume={31},
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year={2018}
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}
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@inproceedings{patrini2017making,
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title={Making Deep Neural Networks Robust to Label Noise: {A} Loss Correction Approach},
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author={Patrini, Giorgio and Rozza, Alessandro and Krishna Menon, Aditya and Nock, Richard and Qu, Lizhen},
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booktitle={Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition},
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pages={1944--1952},
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year={2017}
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}
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@inproceedings{han2018coteaching,
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title={Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels},
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author={Han, Bo and Yao, Quanming and Yu, Xingrui and Niu, Gang and Xu, Miao and Hu, Weihua and Tsang, Ivor and Sugiyama, Masashi},
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booktitle={Advances in Neural Information Processing Systems},
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volume={31},
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year={2018}
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}
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@inproceedings{li2020dividemix,
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title={{DivideMix}: Learning with Noisy Labels as Semi-Supervised Learning},
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author={Li, Junnan and Socher, Richard and Hoi, Steven C. H.},
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booktitle={International Conference on Learning Representations},
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year={2020}
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}
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@inproceedings{ren2018learning,
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title={Learning to Reweight Examples for Robust Deep Learning},
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author={Ren, Mengye and Zeng, Wenyuan and Yang, Bin and Urtasun, Raquel},
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booktitle={Proceedings of the 35th International Conference on Machine Learning},
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pages={4334--4343},
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year={2018}
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}
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@inproceedings{feng2020can,
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title={Can Cross Entropy Loss Be Robust to Label Noise?},
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author={Feng, Lei and Shu, Senlin and Lin, Zhuoyi and Lv, Fengmao and Li, Li and An, Bo},
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booktitle={Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence},
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pages={2206--2212},
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year={2020}
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}
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@inproceedings{zhou2021asymmetric,
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title={Asymmetric Loss Functions for Learning with Noisy Labels},
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author={Zhou, Xiong and Liu, Xianming and Jiang, Junjun and Gao, Xin and Ji, Xiangyang},
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booktitle={Proceedings of the 38th International Conference on Machine Learning},
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pages={12846--12856},
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year={2021}
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}
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@article{hanley1982meaning,
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title={The meaning and use of the area under a receiver operating characteristic ({ROC}) curve},
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author={Hanley, James A and McNeil, Barbara J},
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journal={Radiology},
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volume={143},
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number={1},
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pages={29--36},
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year={1982},
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publisher={Radiological Society of North America}
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}
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@inproceedings{yuan2021auc,
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title={Large-scale Robust Deep {AUC} Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification},
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author={Yuan, Zhuoning and Yan, Yan and Sonka, Milan and Yang, Tianbao},
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booktitle={Proceedings of the {IEEE/CVF} International Conference on Computer Vision ({ICCV})},
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pages={3040--3049},
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year={2021}
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}
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@article{neyman1933ix,
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title={On the problem of the most efficient tests of statistical hypotheses},
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author={Neyman, Jerzy and Pearson, Egon S.},
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journal={Philosophical Transactions of the Royal Society of London. Series {A}},
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volume={231},
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pages={289--337},
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year={1933},
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publisher={The Royal Society}
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}
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@inproceedings{wang2017chestxray14,
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title={{ChestX-ray8}: Hospital-Scale Chest {X-Ray} Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases},
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author={Wang, Xiaosong and Peng, Yifan and Lu, Le and Lu, Zhiyong and Bagheri, Mohammadhadi and Summers, Ronald M.},
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booktitle={Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition ({CVPR})},
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pages={2097--2106},
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year={2017},
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doi={10.1109/CVPR.2017.369}
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}
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@article{oakdenrayner2020exploring,
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title={Exploring Large-scale Public Medical Image Datasets},
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author={Oakden-Rayner, Luke},
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journal={Academic Radiology},
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volume={27},
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number={1},
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pages={106--112},
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year={2020},
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doi={10.1016/j.acra.2019.10.006},
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publisher={Elsevier}
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}
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@article{alcalafdez2011keel,
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title={{KEEL} Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework},
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author={Alcal{\'a}-Fdez, J. and Fern{\'a}ndez, A. and Luengo, J. and Derrac, J. and Garc{\'i}a, S. and S{\'a}nchez, L. and Herrera, F.},
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journal={Journal of Multiple-Valued Logic and Soft Computing},
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volume={17},
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number={2--3},
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pages={255--287},
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year={2011},
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publisher={Old City Publishing}
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}
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@inproceedings{wei2022cifar10n,
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title={Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations},
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author={Wei, Jiaheng and Zhu, Zhaowei and Cheng, Hao and Liu, Tongliang and Niu, Gang and Liu, Yang},
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booktitle={International Conference on Learning Representations ({ICLR})},
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year={2022},
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url={https://openreview.net/forum?id=TBWA6PLJZQm}
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}
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@article{moro2014bank,
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title={A Data-Driven Approach to Predict the Success of Bank Telemarketing},
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author={Moro, S{\'e}rgio and Cortez, Paulo and Rita, Paulo},
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journal={Decision Support Systems},
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volume={62},
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pages={22--31},
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year={2014},
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doi={10.1016/j.dss.2014.03.001},
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publisher={Elsevier}
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}
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@inproceedings{dalpozzolo2015calibrating,
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title={Calibrating Probability with Undersampling for Unbalanced Classification},
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author={Dal Pozzolo, Andrea and Caelen, Olivier and Johnson, Reid A. and Bontempi, Gianluca},
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booktitle={2015 {IEEE} Symposium Series on Computational Intelligence ({SSCI})},
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pages={159--166},
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year={2015},
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doi={10.1109/SSCI.2015.33},
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organization={IEEE}
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}
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@misc{tittelfitz2023fpr,
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title={{FPR} Estimation for Fraud Detection in the Presence of Class-Conditional Label Noise},
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author={Tittelfitz, Justin},
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year={2023},
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eprint={2308.02695},
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archivePrefix={arXiv},
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primaryClass={stat.ML}
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}
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@misc{homecredit2018kaggle,
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title={Home Credit Default Risk},
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author={{Home Credit Group}},
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year={2018},
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howpublished={Kaggle Competition},
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url={https://www.kaggle.com/c/home-credit-default-risk}
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}
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@inproceedings{cohen2022torchxrayvision,
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title={{TorchXRayVision}: A Library of Chest {X-Ray} Datasets and Models},
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author={Cohen, Joseph Paul and Viviano, Joseph D. and Bertin, Paul and Morrison, Paul and Torabian, Parsa and Guarrera, Matteo and Lungren, Matthew P. and Chaudhari, Akshay and Brooks, Rupert and Hashir, Mohammad and Bertrand, Hadrien},
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booktitle={Proceedings of The 5th International Conference on Medical Imaging with Deep Learning ({MIDL})},
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series={Proceedings of Machine Learning Research},
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volume={172},
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pages={231--249},
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year={2022},
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publisher={{PMLR}}
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}
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@article{parzen1962estimation,
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title={On estimation of a probability density function and mode},
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author={Parzen, Emanuel},
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journal={The Annals of Mathematical Statistics},
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volume={33},
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number={3},
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pages={1065--1076},
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year={1962},
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publisher={Institute of Mathematical Statistics}
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}
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@inproceedings{cortes2003auc,
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title={{AUC} Optimization vs. Error Rate Minimization},
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author={Cortes, Corinna and Mohri, Mehryar},
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booktitle={Advances in Neural Information Processing Systems},
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volume={16},
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year={2003}
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}
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@inproceedings{lin2017focal,
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title={Focal Loss for Dense Object Detection},
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author={Lin, Tsung-Yi and Goyal, Priya and Girshick, Ross and He, Kaiming and Doll{\'a}r, Piotr},
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booktitle={Proceedings of the {IEEE} International Conference on Computer Vision ({ICCV})},
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pages={2999--3007},
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year={2017},
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doi={10.1109/ICCV.2017.324}
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}
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@inproceedings{lee2018cleannet,
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title={{CleanNet}: Transfer Learning for Scalable Image Classifier Training with Label Noise},
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author={Lee, Kuang-Huai and He, Xiaodong and Zhang, Lei and Yang, Linjun},
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booktitle={Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition ({CVPR})},
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pages={5447--5456},
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year={2018}
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}
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