This repository implements the MPTS (Model Predictive Task Sampling) and baselines proposed in the paper "Model Predictive Task Sampling for Efficient and Robust Adaptation".
Framework of MPTS in Adaptation Learning:
Please refer to the following folders for related instructions and code:
sinusoid/: Code related to sinusoid regressionMetaRL/: Code related to Meta RL scenariosDR/: Code related to Domain Randomization scenarios
K-shot Sinusoid Regression Results (10 Runs):
Get started with our quickstart.ipynb notebook, which provides an interactive introduction to the MPTS approach using the sinusoid regression scenario.
If you find this work useful for your research, please consider referring to our paper:
@article{wang2025model,
title={Model predictive task sampling for efficient and robust adaptation},
author={Wang, Qi Cheems and Xiao, Zehao and Mao, Yixiu and Qu, Yun and Shen, Jiayi and Lv, Yiqin and Ji, Xiangyang},
journal={arXiv preprint arXiv:2501.11039},
year={2025}
}The following includes key baselines and relevant literature. If you are interested in robust fast adaptation methods, please consider referring to them.
@inproceedings{he2024robust,
title={Robust Multi-Task Learning with Excess Risks},
author={He, Yifei and Zhou, Shiji and Zhang, Guojun and Yun, Hyokun and Xu, Yi and Zeng, Belinda and Chilimbi, Trishul and Zhao, Han},
booktitle={International Conference on Machine Learning},
pages={18094--18114},
year={2024},
organization={PMLR}
}
@article{wang2024towards,
title={Towards task sampler learning for meta-learning},
author={Wang, Jingyao and Qiang, Wenwen and Su, Xingzhe and Zheng, Changwen and Sun, Fuchun and Xiong, Hui},
journal={International Journal of Computer Vision},
volume={132},
number={12},
pages={5534--5564},
year={2024},
publisher={Springer}
}
@inproceedings{liu2020adaptive,
title={Adaptive task sampling for meta-learning},
author={Liu, Chenghao and Wang, Zhihao and Sahoo, Doyen and Fang, Yuan and Zhang, Kun and Hoi, Steven CH},
booktitle={European Conference on Computer Vision},
pages={752--769},
year={2020},
organization={Springer}
}
@inproceedings{toloubidokhti2023dats,
title={Dats: Difficulty-aware task sampler for meta-learning physics-informed neural networks},
author={Toloubidokhti, Maryam and Ye, Yubo and Missel, Ryan and Jiang, Xiajun and Kumar, Nilesh and Shrestha, Ruby and Wang, Linwei},
booktitle={The Twelfth International Conference on Learning Representations},
year={2023}
}
@article{yao2021meta,
title={Meta-learning with an adaptive task scheduler},
author={Yao, Huaxiu and Wang, Yu and Wei, Ying and Zhao, Peilin and Mahdavi, Mehrdad and Lian, Defu and Finn, Chelsea},
journal={Advances in Neural Information Processing Systems},
volume={34},
pages={7497--7509},
year={2021}
}
@article{kaddour2020probabilistic,
title={Probabilistic active meta-learning},
author={Kaddour, Jean and S{\ae}mundsson, Steind{\'o}r and others},
journal={Advances in Neural Information Processing Systems},
volume={33},
pages={20813--20822},
year={2020}
}
@inproceedings{kumar2023effect,
title={The effect of diversity in meta-learning},
author={Kumar, Ramnath and Deleu, Tristan and Bengio, Yoshua},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={37},
number={7},
pages={8396--8404},
year={2023}
}
@article{greenberg2024train,
title={Train hard, fight easy: Robust meta reinforcement learning},
author={Greenberg, Ido and Mannor, Shie and Chechik, Gal and Meirom, Eli},
journal={Advances in Neural Information Processing Systems},
volume={36},
year={2024}
}
@inproceedings{sagawa2019distributionally,
title={Distributionally Robust Neural Networks},
author={Sagawa, Shiori and Koh, Pang Wei and Hashimoto, Tatsunori B and Liang, Percy},
booktitle={International Conference on Learning Representations},
year={2019}
}
@article{wang2024simple,
title={A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm},
author={Wang, Qi and Lv, Yiqin and Xie, Zheng and Huang, Jincai and others},
journal={Advances in Neural Information Processing Systems},
volume={36},
year={2024}
}