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Research

For technical details, please check our research publications.

@inproceedings{wang2021flaml,
title={FLAML: A Fast and Lightweight AutoML Library},
author={Chi Wang and Qingyun Wu and Markus Weimer and Erkang Zhu},
year={2021},
booktitle={MLSys},
}
@inproceedings{wu2021cfo,
title={Frugal Optimization for Cost-related Hyperparameters},
author={Qingyun Wu and Chi Wang and Silu Huang},
year={2021},
booktitle={AAAI},
}
@inproceedings{wang2021blendsearch,
title={Economical Hyperparameter Optimization With Blended Search Strategy},
author={Chi Wang and Qingyun Wu and Silu Huang and Amin Saied},
year={2021},
booktitle={ICLR},
}
@inproceedings{liuwang2021hpolm,
title={An Empirical Study on Hyperparameter Optimization for Fine-Tuning Pre-trained Language Models},
author={Susan Xueqing Liu and Chi Wang},
year={2021},
booktitle={ACL},
}
@inproceedings{wu2021chacha,
title={ChaCha for Online AutoML},
author={Qingyun Wu and Chi Wang and John Langford and Paul Mineiro and Marco Rossi},
year={2021},
booktitle={ICML},
}
  • Fair AutoML. Qingyun Wu, Chi Wang. ArXiv preprint arXiv:2111.06495 (2021).
@inproceedings{wuwang2021fairautoml,
title={Fair AutoML},
author={Qingyun Wu and Chi Wang},
year={2021},
booktitle={ArXiv preprint arXiv:2111.06495},
}
@inproceedings{kayaliwang2022default,
title={Mining Robust Default Configurations for Resource-constrained AutoML},
author={Moe Kayali and Chi Wang},
year={2022},
booktitle={ArXiv preprint arXiv:2202.09927},
}
@inproceedings{
zhang2023targeted,
title={Targeted Hyperparameter Optimization with Lexicographic Preferences Over Multiple Objectives},
author={Shaokun Zhang and Feiran Jia and Chi Wang and Qingyun Wu},
booktitle={International Conference on Learning Representations},
year={2023},
url={https://openreview.net/forum?id=0Ij9_q567Ma}
}