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Multi-objective optimization (MOO) has become an influential framework in many machine learning problems with multiple objectives such as learning with multiple criteria and multi-task learning (MTL).
Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Gradient-based learning applied to document recognition
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Efficient projections onto the l 1-ball for learning in high dimensions
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Multiple-gradient descent algorithm (mgda) for multiobjective optimization
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Pushmeet Kohli Nathan Silberman, Derek Hoiem and Rob Fergus · 2012
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Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes
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Projection onto the probability simplex: An efficient algorithm with a simple proof, and an application, 2013
Weiran Wang and Miguel Á. Carreira-Perpiñán · 2013
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A very complicated proof of the minimax theorem
Jonathan M Borwein · 2016
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The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
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Kazuma Hashimoto, Caiming Xiong, Yoshimasa Tsuruoka, and Richard Socher · 2016
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Cross-stitch networks for multi-task learning
Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, and Martial Hebert · 2016
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Segnet: A deep convolutional encoder-decoder architecture for image segmentation
Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla · 2017
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Routing networks: Adaptive selection of non-linear functions for multi-task learning
Clemens Rosenbaum, Tim Klinger, and Matthew Riemer · 2017
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An overview of multi-task learning in deep neural networks
Sebastian Ruder · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks
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Dynamic task prioritization for multitask learning
Michelle Guo, Albert Haque, De-An Huang, Serena Yeung, and Li Fei-Fei · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
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Alex Kendall, Yarin Gal, and Roberto Cipolla · 2018
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Modeling task relationships in multi-task learning with multi-gate mixture-of-experts
Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H Chi · 2018
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Reasonable effectiveness of random weighting: A litmus test for multi-task learning
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The stochastic multi-gradient algorithm for multi-objective optimization and its application to supervised machine learning
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Mtrl - multi task rl algorithms
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Multi-objective spibb: Seldonian offline policy improvement with safety constraints in finite mdps
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Multi-task learning for dense prediction tasks: A survey
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Ozan Sener and Vladlen Koltun · 2018
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The apolloscape open dataset for autonomous driving and its application
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Pareto multi-task learning
Xi Lin, Hui-Ling Zhen, Zhenhua Li, Qing-Fu Zhang, and Sam Kwong · 2019
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End-to-end multi-task learning with attention
Shikun Liu, Edward Johns, and Andrew J Davison · 2019
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Attentive single-tasking of multiple tasks
Kevis-Kokitsi Maninis, Ilija Radosavovic, and Iasonas Kokkinos · 2019
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Just pick a sign: Optimizing deep multitask models with gradient sign dropout
Zhao Chen, Jiquan Ngiam, Yanping Huang, Thang Luong, Henrik Kretzschmar, Yuning Chai, and Dragomir Anguelov · 2020
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The traveling observer model: Multi-task learning through spatial variable embeddings
Elliot Meyerson and Risto Miikkulainen · 2020
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Bridging multi-task learning and meta-learning: Towards efficient training and effective adaptation
Haoxiang Wang, Han Zhao, and Bo Li · 2021
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Multi-objective meta learning
Feiyang Ye, Baijiong Lin, Zhixiong Yue, Pengxin Guo, Qiao Xiao, and Yu Zhang · 2021
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A survey on multi-task learning
Yu Zhang and Qiang Yang · 2021
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A multi-objective/multi-task learning framework induced by pareto stationarity
Michinari Momma, Chaosheng Dong, and Jia Liu · 2022
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Multi-task learning as a bargaining game
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On the convergence of stochastic multi-objective gradient manipulation and beyond
Shiji Zhou, Wenpeng Zhang, Jiyan Jiang, Wenliang Zhong, Jinjie Gu, and Wenwu Zhu · 2022
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Three-way trade-off in multi-objective learning: Optimization, generalization and conflict-avoidance
Lisha Chen, Heshan Fernando, Yiming Ying, and Tianyi Chen · 2023
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Mitigating gradient bias in multi-objective learning: A provably convergent approach
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Revisiting scalarization in multi-task learning: A theoretical perspective
Yuzheng Hu, Ruicheng Xian, Qilong Wu, Qiuling Fan, Lang Yin, and Han Zhao · 2023
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Famo: Fast adaptive multitask optimization, 2023
Bo Liu, Yihao Feng, Peter Stone, and Qiang Liu · 2023
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