[33] Kristof Van Moffaert and Ann Nowé. Multi-objective reinforcement learning using sets of pareto dominating policies. Journal of Machine Learning Research, 15(107):3663-3692, 2014.
[34] Michinari Momma, Chaosheng Dong, and Jia Liu. A multi-objective/multi-task learning framework induced by pareto stationarity. In Proc. International Conference on Machine Learning, pages 15895-15907, Baltimore, MD, 2022.
[35] Aviv Navon, Aviv Shamsian, Ethan Fetaya, and Gal Chechik. Learning the pareto front with hypernetworks. In Proc. International Conference on Learning Representations, virtual, April 2020.
[36] Yurii Nesterov. Introductory Lectures on Convex Optimization: A Basic Course. Springer Publishing Company, Incorporated, 1 edition, 2014.
[37] Dominic Richards and Ilja Kuzborskij. Stability & generalisation of gradient descent for shallow neural networks without the neural tangent kernel. In Proc. Advances in Neural Information Processing Systems, volume 34, virtual, 2021.
[38] Sebastian Ruder. An overview of multi-task learning in deep neural networks. arXiv preprint:1706.05098, 2017.
[39] Ozan Sener and Vladlen Koltun. Multi-task learning as multi-objective optimization. In Proc. Advances in Neural Information Processing Systems, Montreal, Canada, December 2018.
[40] Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet S Talwalkar. Federated multi-task learning. In I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Proc. Advances in Neural Information Processing Systems, volume 30, Long Beach, CA, 2017.
[41] Peter Súkeník and Christoph H Lampert. Generalization in multi-objective machine learning. arXiv preprint arXiv:2208.13499, 2022.
[42] Hiroki Tanabe, Ellen H. Fukuda, and Nobuo Yamashita. Proximal gradient methods for multi-objective optimization and their applications. Computational Optimization and Applications, 72(2):339-361, 2019.
[43] Simon Vandenhende, Stamatos Georgoulis, Wouter Van Gansbeke, Marc Proesmans, Dengxin Dai, and Luc Van Gool. Multi-task learning for dense prediction tasks: A survey. IEEE Trans. Pattern Anal. Machine Intell., 2021.
[44] Peiyao Xiao, Hao Ban, and Kaiyi Ji. Direction-oriented multi-objective learning: Simple and provable stochastic algorithms. In Proc. Advances in Neural Information Processing Systems, New Orleans, LA, 2023.
[45] Derrick Xin, Behrooz Ghorbani, Justin Gilmer, Ankush Garg, and Orhan First. Do current multi-task optimization methods in deep learning even help? In Proc. Advances in Neural Information Processing Systems, volume 35, pages 13597-13609, New Orleans, LA, 2022.
[46] Yijun Yang, Jing Jiang, Tianyi Zhou, Jie Ma, and Yuhui Shi. Pareto policy pool for model-based offline reinforcement learning. In Proc. International Conference on Learning Representations, virtual, 2021.
[47] Feiyang Ye, Baijiong Lin, Zhixiong Yue, Pengxin Guo, Qiao Xiao, and Yu Zhang. Multi-objective meta learning. In Proc. Advances in Neural Information Processing Systems, volume 34, pages 21338-21351, virtual, 2021.
[48] Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn. Gradient surgery for multi-task learning. In Proc. Advances in Neural Information Processing Systems, virtual, December 2020.
[49] Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rogriguez, and Krishna P Gummadi. Fairness constraints: Mechanisms for fair classification. In Proc. International Conference on Artificial Intelligence and Statistics, pages 962–970, Fort Lauderdale, FL, 2017.