[35] Baijiong Lin, Feiyang Ye, Yu Zhang, and Ivor Wai-Hung Tsang. Reasonable effectiveness of random weighting: A Litmus test for multi-task learning. In IEEE Transactions on Machine Learning Research (T-MLR), 2022. 8
[36] Xi Lin, Hui-Ling Zhen, Zhenhua Li, Qingfu Zhang, and Sam Tak Wu Kwong. Pareto multi-task learning. In Conference on Neural Information Processing Systems (NeurIPS), 2019. 4
[37] Bo Liu, Xingchao Liu, Xiaojie Jin, Peter Stone, and Qiang Liu. Conflict-averse gradient descent for multi-task learning. Conference on Neural Information Processing Systems (NeurIPS), 34: 18878–18890, 2021. 1, 2, 4
[38] Liyang Liu, Yi Li, Zhanghui Kuang, Jing-Hao Xue, Yimin Chen, Wenming Yang, Qingmin Liao, and Wayne Zhang. Towards impartial multi-task learning. In International Conference on Learning Representations (ICLR), 2021. 2, 4, 18
[39] Shikun Liu, Edward Johns, and Andrew J. Davison. End-to-end multi-task learning with attention. Conference on Computer Vision and Pattern Recognition (CVPR), pages 1871-1880, 2018. 2
[40] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaou Tang. Deep learning face attributes in the wild. In International Conference on Computer Vision (ICCV), 2015. 4, 5
[41] Raphael Gontijo Lopes, Sylvia J. Smullin, Ekin Dogus Cubuk, and Ethan Dyer. Tradeoffs in data augmentation: An empirical study. In International Conference on Learning Representations (ICLR), 2021. 17
[42] Debabrata Mahapatra and Vaibhav Rajan. Multi-task learning with user preferences: Gradient descent with controlled ascent in Pareto optimization. In International Conference on Machine Learning (ICML), 2020. 3, 4
[43] Lincy Mathews and Seetha Hari. Learning from imbalanced data. Advances in Computer and Electrical Engineering, 2019. 3
[44] Andreas Maurer, Massimiliano Pontil, and Bernardino Romero-Paredes. The benefit of multitask representation learning. Journal of Machine Learning Research (JMLR), abs/1505.06279, 2015. 1
[45] Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, and Martial Hebert. Cross-stitch networks for multi-task learning. In Conference on Computer Vision and Pattern Recognition (CVPR), pages 3994-4003, 2016. 2
[46] Jack Parker-Holder, Vu Nguyen, and Stephen J. Roberts. Provably efficient online hyperparameter optimization with population-based bandits. arXiv: Learning, 2020. 8, 20
[47] Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang. Moment matching for multi-source domain adaptation. In International Conference on Computer Vision (ICCV), pages 1406–1415, 2018. 4, 5, 15, 16
[48] Anastasia Pentina and Christoph H. Lampert. Multi-task learning with labeled and unlabeled tasks. In International Conference on Machine Learning (ICML), 2016. 1
[49] Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi. Learning multiple visual domains with residual adapters. Conference on Neural Information Processing Systems (NeurIPS), 30, 2017. 3
[50] Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi. Efficient parametrization of multidomain deep neural networks. In Conference on Computer Vision and Pattern Recognition (CVPR), pages 8119-8127, 2018. 3
[51] Amir Rosenfeld and John K Tsotsos. Incremental learning through deep adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 42(3):651-663, 2018. 3
[52] Michael Ruchte and Josif Grabocka. Multi-task problems are not multi-objective. ArXiv, abs/2110.07301, 2021. 4, 10