[13] Krzysztof Dembczyński, Wojciech Kotlowski, Oluwasanmi Koyejo, and Nagarajan Natarajan. Consistency analysis for binary classification revisited. In Proceedings of the 34th International Conference on Machine Learning (ICML 2017), volume 70 of Proceedings of Machine Learning Research, pages 961–969. PMLR, 2017.
[14] Jia Deng, Sanjeev Satheesh, Alexander C. Berg, and Fei-Fei Li. Fast and balanced: Efficient label tree learning for large scale object recognition. In Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, Granada, Spain., pages 567-575, 2011.
[15] Himanshu Jain, Yashoteja Prabhu, and Manik Varma. Extreme multi-label loss functions for recommendation, tagging, ranking and other missing label applications. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '16, page 935-944. Association for Computing Machinery, 2016. ISBN 9781450342322.
[16] Kalina Jasinska, Krzysztof Dembczynski, Róbert Busa-Fekete, Karlson Pfannschmidt, Timo Klerx, and Eyke Hüllermeier. Extreme F-measure maximization using sparse probability estimates. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, volume 48 of JMLR Workshop and Conference Proceedings, pages 1435-1444. JMLR.org, 2016.
[17] Kalina Jasinska-Kobus, Marek Wydmuch, Krzysztof Dembczyński, Mikhail Kuznetsov, and Róbert Busa-Fekete. Probabilistic label trees for extreme multi-label classification. CoRR, abs/2009.11218, 2020.
[18] Ting Jiang, Deqing Wang, Leilei Sun, Huayi Yang, Zhengyang Zhao, and Fuzhen Zhuang. Lightxml: Transformer with dynamic negative sampling for high-performance extreme multi-label text classification. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 7987-7994, 2021.
[19] Sujay Khandagale, Han Xiao, and Rohit Babbar. Bonsai: diverse and shallow trees for extreme multi-label classification. Machine Learning, 109(11):2099-2119, 2020.
[20] Siddhant Kharbanda, Atmadeep Banerjee, Erik Schultheis, and Rohit Babbar. Cascadexml: Rethinking transformers for end-to-end multi-resolution training in extreme multi-label classification. Advances in Neural Information Processing Systems, 35:2074-2087, 2022.
[21] Siddhant Kharbanda, Devaansh Gupta, Erik Schultheis, Atmadeep Banerjee, Vikas Verma, and Rohit Babbar. Gandalf: Data augmentation is all you need for extreme classification. 2022.
[22] Wojciech Kotlowski and Krzysztof Dembczyński. Surrogate regret bounds for generalized classification performance metrics. Machine Learning, 10:549-572, 2017.
[23] Oluwasanmi Koyejo, Nagarajan Natarajan, Pradeep Ravikumar, and Inderjit S. Dhillon. Consistent multilabel classification. In Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada, pages 3321-3329, 2015.
[24] Siu Kwan Lam, Antoine Pitrou, and Stanley Seibert. Numba: A llvm-based python JIT compiler. In Proceedings of the Second Workshop on the LLVM Compiler Infrastructure in HPC, pages 1-6, 2015.
[25] David D. Lewis. Evaluating and optimizing autonomous text classification systems. In SIGIR'95, Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Seattle, Washington, USA, July 9-13, 1995 (Special Issue of the SIGIR Forum), pages 246-254. ACM Press, 1995.
[26] Yu-Jen Lin and Chih-Jen Lin. On the thresholding strategy for infrequent labels in multi-label classification. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM '23, page 1441-1450, New York, NY, USA, 2023. Association for Computing Machinery.