===============multi-task learning===============
@book{caruana1998multitask,
	title={Multitask learning},
	author={Caruana, Rich},
	year={1998},
	publisher={Springer}
}

@article{ruder2017overview,
	title={An overview of multi-task learning in deep neural networks},
	author={Ruder, Sebastian},
	journal={ArXiv Preprint ArXiv:1706.05098},
	year={2017}
}

@article{zhang2021survey,
	title={A survey on multi-task learning},
	author={Zhang, Yu and Yang, Qiang},
	journal={IEEE Transactions on Knowledge and Data Engineering},
	volume={34},
	number={12},
	pages={5586--5609},
	year={2021},
	publisher={IEEE}
}

@article{swersky2013multi,
	title={Multi-task bayesian optimization},
	author={Swersky, Kevin and Snoek, Jasper and Adams, Ryan P},
	journal={Advances in Neural Information Processing Systems},
	volume={26},
	year={2013}
}

@article{kurin2022defense,
	title={In defense of the unitary scalarization for deep multi-task learning},
	author={Kurin, Vitaly and De Palma, Alessandro and Kostrikov, Ilya and Whiteson, Shimon and Kumar, M Pawan},
	journal={ArXiv Preprint ArXiv:2201.04122},
	year={2022}
}

@article{lin2021closer,
	title={A closer look at loss weighting in multi-task learning},
	author={Lin, Baijiong and Ye, Feiyang and Zhang, Yu},
	journal={ArXiv Preprint ArXiv:2111.10603},
	year={2021}
}

@inproceedings{tang2020progressive,
  title={Progressive layered extraction (ple): A novel multi-task learning (mtl) model for personalized recommendations},
  author={Tang, Hongyan and Liu, Junning and Zhao, Ming and Gong, Xudong},
  booktitle={ACM Conference on Recommender Systems},
  pages={269--278},
  year={2020}
}

===============model structure===============
@inproceedings{misra2016cross,
	title={Cross-stitch networks for multi-task learning},
	author={Misra, Ishan and Shrivastava, Abhinav and Gupta, Abhinav and Hebert, Martial},
	booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
	pages={3994--4003},
	year={2016}
}

@inproceedings{ma2018modeling,
	title={Modeling task relationships in multi-task learning with multi-gate mixture-of-experts},
	author={Ma, Jiaqi and Zhao, Zhe and Yi, Xinyang and Chen, Jilin and Hong, Lichan and Chi, Ed H},
	booktitle={ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
	pages={1930--1939},
	year={2018}
}

===============applications===============
@article{yang2020multi,
	title={Multi-task reinforcement learning with soft modularization},
	author={Yang, Ruihan and Xu, Huazhe and Wu, Yi and Wang, Xiaolong},
	journal={Advances in Neural Information Processing Systems},
	volume={33},
	pages={4767--4777},
	year={2020}
}

@inproceedings{zhao2019recommending,
	title={Recommending what video to watch next: a multitask ranking system},
	author={Zhao, Zhe and Hong, Lichan and Wei, Li and Chen, Jilin and Nath, Aniruddh and Andrews, Shawn and Kumthekar, Aditee and Sathiamoorthy, Maheswaran and Yi, Xinyang and Chi, Ed},
	booktitle={ACM Conference on Recommender Systems},
	pages={43--51},
	year={2019}
}

@inproceedings{chowdhuri2019multinet,
	title={Multinet: Multi-modal multi-task learning for autonomous driving},
	author={Chowdhuri, Sauhaarda and Pankaj, Tushar and Zipser, Karl},
	booktitle={IEEE Winter Conference on Applications of Computer Vision},
	pages={1496--1504},
	year={2019},
	organization={IEEE}
}

===============loss balancing algorithms===============
@inproceedings{kendall2018multi,
	title={Multi-task learning using uncertainty to weigh losses for scene geometry and semantics},
	author={Kendall, Alex and Gal, Yarin and Cipolla, Roberto},
	booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
	pages={7482--7491},
	year={2018}
}

@inproceedings{guo2018dynamic,
  title={Dynamic task prioritization for multitask learning},
  author={Guo, Michelle and Haque, Albert and Huang, De-An and Yeung, Serena and Fei-Fei, Li},
  booktitle={European Conference on Computer Vision},
  pages={270--287},
  year={2018}
}

@inproceedings{liu2019end,
	title={End-to-end multi-task learning with attention},
	author={Liu, Shikun and Johns, Edward and Davison, Andrew J},
	booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
	pages={1871--1880},
	year={2019}
}

@article{lin2022reasonable,
	title={Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi-Task Learning},
	author={Lin, Baijiong and Feiyang, YE and Zhang, Yu and Tsang, Ivor},
	journal={Transactions on Machine Learning Research},
	year={2022}
}

@article{ye2021multi,
	title={Multi-objective meta learning},
	author={Ye, Feiyang and Lin, Baijiong and Yue, Zhixiong and Guo, Pengxin and Xiao, Qiao and Zhang, Yu},
	journal={Advances in Neural Information Processing Systems},
	volume={34},
	pages={21338--21351},
	year={2021}
}

===============gradient balancing algorithms===============
@inproceedings{chen2018gradnorm,
	title={Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks},
	author={Chen, Zhao and Badrinarayanan, Vijay and Lee, Chen-Yu and Rabinovich, Andrew},
	booktitle={International Conference on Machine Learning},
	pages={794--803},
	year={2018},
	organization={PMLR}
}

@article{sener2018multi,
	title={Multi-task learning as multi-objective optimization},
	author={Sener, Ozan and Koltun, Vladlen},
	journal={Advances in Neural Information Processing Systems},
	volume={31},
	year={2018}
}

@article{liu2021conflict,
	title={Conflict-averse gradient descent for multi-task learning},
	author={Liu, Bo and Liu, Xingchao and Jin, Xiaojie and Stone, Peter and Liu, Qiang},
	journal={Advances in Neural Information Processing Systems},
	volume={34},
	pages={18878--18890},
	year={2021}
}

@article{yu2020gradient,
	title={Gradient surgery for multi-task learning},
	author={Yu, Tianhe and Kumar, Saurabh and Gupta, Abhishek and Levine, Sergey and Hausman, Karol and Finn, Chelsea},
	journal={Advances in Neural Information Processing Systems},
	volume={33},
	pages={5824--5836},
	year={2020}
}

@article{chen2020just,
	title={Just pick a sign: Optimizing deep multitask models with gradient sign dropout},
	author={Chen, Zhao and Ngiam, Jiquan and Huang, Yanping and Luong, Thang and Kretzschmar, Henrik and Chai, Yuning and Anguelov, Dragomir},
	journal={Advances in Neural Information Processing Systems},
	volume={33},
	pages={2039--2050},
	year={2020}
}

@inproceedings{liu2021towards,
	title={Towards impartial multi-task learning},
	author={Liu, Liyang and Li, Yi and Kuang, Zhanghui and Xue, J and Chen, Yimin and Yang, Wenming and Liao, Qingmin and Zhang, Wayne},
	booktitle={International Conference on Learning Representations},
	year={2021}
}

@article{vandenhende2021multi,
	title={Multi-task learning for dense prediction tasks: A survey},
	author={Vandenhende, Simon and Georgoulis, Stamatios and Van Gansbeke, Wouter and Proesmans, Marc and Dai, Dengxin and Van Gool, Luc},
	journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume={44},
	number={7},
	pages={3614--3633},
	year={2021},
	publisher={IEEE}
}

@inproceedings{navon2022multi,
	title={Multi-Task Learning as a Bargaining Game},
	author={Navon, Aviv and Shamsian, Aviv and Achituve, Idan and Maron, Haggai and Kawaguchi, Kenji and Chechik, Gal and Fetaya, Ethan},
	booktitle={International Conference on Machine Learning},
	pages={16428--16446},
	year={2022},
	organization={PMLR}
}

===============reinforcement learning===============
@article{williams1992simple,
  title={Simple statistical gradient-following algorithms for connectionist reinforcement learning},
  author={Williams, Ronald J},
  journal={Reinforcement learning},
  pages={5--32},
  year={1992},
  publisher={Springer}
}

@article{kaelbling1996reinforcement,
	title={Reinforcement learning: A survey},
	author={Kaelbling, Leslie Pack and Littman, Michael L and Moore, Andrew W},
	journal={Journal of Artificial Intelligence Research},
	volume={4},
	pages={237--285},
	year={1996}
}

@book{watkins1989learning,
	title={Learning from delayed rewards},
	author={Watkins, Christopher John Cornish Hellaby},
	year={1989},
	publisher={King's College, Cambridge United Kingdom}
}

@inproceedings{ziebart2008maximum,
	title={Maximum entropy inverse reinforcement learning.},
	author={Ziebart, Brian D and Maas, Andrew L and Bagnell, J Andrew and Dey, Anind K and others},
	booktitle={AAAI Conference on Artificial Intelligence},
	volume={8},
	pages={1433--1438},
	year={2008},
	organization={Chicago, IL, USA}
}

@article{mnih2013playing,
	title={Playing atari with deep reinforcement learning},
	author={Mnih, Volodymyr and Kavukcuoglu, Koray and Silver, David and Graves, Alex and Antonoglou, Ioannis and Wierstra, Daan and Riedmiller, Martin},
	journal={ArXiv Preprint ArXiv:1312.5602},
	year={2013}
}

@article{arulkumaran2017deep,
	title={Deep reinforcement learning: A brief survey},
	author={Arulkumaran, Kai and Deisenroth, Marc Peter and Brundage, Miles and Bharath, Anil Anthony},
	journal={IEEE Signal Processing Magazine},
	volume={34},
	number={6},
	pages={26--38},
	year={2017},
	publisher={IEEE}
}

@inproceedings{haarnoja2018soft,
	title={Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor},
	author={Haarnoja, Tuomas and Zhou, Aurick and Abbeel, Pieter and Levine, Sergey},
	booktitle={International Conference on Machine Learning},
	pages={1861--1870},
	year={2018},
	organization={PMLR}
}

@article{peters2008reinforcement,
	title={Reinforcement learning of motor skills with policy gradients},
	author={Peters, Jan and Schaal, Stefan},
	journal={Neural Networks},
	volume={21},
	number={4},
	pages={682--697},
	year={2008},
	publisher={Elsevier}
}

===============dataset===============
@article{silberman2012indoor,
	title={Indoor segmentation and support inference from rgbd images.},
	author={Silberman, Nathan and Hoiem, Derek and Kohli, Pushmeet and Fergus, Rob},
	journal={European Conference on Computer Vision},
	volume={7576},
	pages={746--760},
	year={2012}
}

@article{ramakrishnan2014quantum,
	title={Quantum chemistry structures and properties of 134 kilo molecules},
	author={Ramakrishnan, Raghunathan and Dral, Pavlo O and Rupp, Matthias and Von Lilienfeld, O Anatole},
	journal={Scientific Data},
	volume={1},
	number={1},
	pages={1--7},
	year={2014},
	publisher={Nature Publishing Group}
}

===============others===============
@article{fey2019fast,
	title={Fast graph representation learning with PyTorch Geometric},
	author={Fey, Matthias and Lenssen, Jan Eric},
	journal={ArXiv Preprint ArXiv:1903.02428},
	year={2019}
}
@inproceedings{gilmer2017neural,
	title={Neural message passing for quantum chemistry},
	author={Gilmer, Justin and Schoenholz, Samuel S and Riley, Patrick F and Vinyals, Oriol and Dahl, George E},
	booktitle={International Conference on Machine Learning},
	pages={1263--1272},
	year={2017},
	organization={PMLR}
}
@article{vinyals2015order,
	title={Order matters: Sequence to sequence for sets},
	author={Vinyals, Oriol and Bengio, Samy and Kudlur, Manjunath},
	journal={ArXiv Preprint ArXiv:1511.06391},
	year={2015}
}
@article{badrinarayanan2017segnet,
	title={Segnet: A deep convolutional encoder-decoder architecture for image segmentation},
	author={Badrinarayanan, Vijay and Kendall, Alex and Cipolla, Roberto},
	journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume={39},
	number={12},
	pages={2481--2495},
	year={2017},
	publisher={IEEE}
}

@article{wu2020comprehensive,
	title={A comprehensive survey on graph neural networks},
	author={Wu, Zonghan and Pan, Shirui and Chen, Fengwen and Long, Guodong and Zhang, Chengqi and Philip, S Yu},
	journal={IEEE transactions on Neural Networks and Learning Systems},
	volume={32},
	number={1},
	pages={4--24},
	year={2020},
	publisher={IEEE}
}

@article{kingma2014adam,
	title={Adam: A method for stochastic optimization},
	author={Kingma, Diederik P and Ba, Jimmy},
	journal={ArXiv Preprint ArXiv:1412.6980},
	year={2014}
}

@article{maron2019provably,
	title={Provably powerful graph networks},
	author={Maron, Haggai and Ben-Hamu, Heli and Serviansky, Hadar and Lipman, Yaron},
	journal={Advances in Neural Information Processing Systems},
	volume={32},
	year={2019}
}

@article{gasteiger2020directional,
	title={Directional message passing for molecular graphs},
	author={Gasteiger, Johannes and Gro{\ss}, Janek and G{\"u}nnemann, Stephan},
	journal={ArXiv Preprint ArXiv:2003.03123},
	year={2020}
}

@article{liu2022auto,
  title={Auto-lambda: Disentangling dynamic task relationships},
  author={Liu, Shikun and James, Stephen and Davison, Andrew J and Johns, Edward},
  journal={ArXiv Preprint ArXiv:2202.03091},
  year={2022}
}

@article{chen2021multi,
  title={A multi-task-learning-based transfer deep reinforcement learning design for autonomic optical networks},
  author={Chen, Xiaoliang and Proietti, Roberto and Liu, Che-Yu and Yoo, SJ Ben},
  journal={IEEE Journal on Selected Areas in Communications},
  volume={39},
  number={9},
  pages={2878--2889},
  year={2021},
  publisher={IEEE}
}

@article{kalashnikov2021mt,
  title={Mt-opt: Continuous multi-task robotic reinforcement learning at scale},
  author={Kalashnikov, Dmitry and Varley, Jacob and Chebotar, Yevgen and Swanson, Benjamin and Jonschkowski, Rico and Finn, Chelsea and Levine, Sergey and Hausman, Karol},
  journal={ArXiv Preprint ArXiv:2104.08212},
  year={2021}
}
