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169012f3e7
* improve max_valid_n and doc * Update README.md Co-authored-by: Li Jiang <lijiang1@microsoft.com> * add support for chatgpt * notebook * newline at end of file * chatgpt notebook * ChatGPT in Azure * doc * math * warning, timeout, log file name * handle import error * doc update; default value * paper * doc * docstr * eval_func * prompt and messages * remove confusing words * notebook name --------- Co-authored-by: Li Jiang <lijiang1@microsoft.com> Co-authored-by: Susan Xueqing Liu <liususan091219@users.noreply.github.com>
3.9 KiB
3.9 KiB
Research
For technical details, please check our research publications.
- FLAML: A Fast and Lightweight AutoML Library. Chi Wang, Qingyun Wu, Markus Weimer, Erkang Zhu. MLSys 2021.
@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},
}
- Frugal Optimization for Cost-related Hyperparameters. Qingyun Wu, Chi Wang, Silu Huang. AAAI 2021.
@inproceedings{wu2021cfo,
title={Frugal Optimization for Cost-related Hyperparameters},
author={Qingyun Wu and Chi Wang and Silu Huang},
year={2021},
booktitle={AAAI},
}
- Economical Hyperparameter Optimization With Blended Search Strategy. Chi Wang, Qingyun Wu, Silu Huang, Amin Saied. ICLR 2021.
@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},
}
- An Empirical Study on Hyperparameter Optimization for Fine-Tuning Pre-trained Language Models. Susan Xueqing Liu, Chi Wang. ACL 2021.
@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},
}
- ChaCha for Online AutoML. Qingyun Wu, Chi Wang, John Langford, Paul Mineiro and Marco Rossi. ICML 2021.
@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},
}
- Mining Robust Default Configurations for Resource-constrained AutoML. Moe Kayali, Chi Wang. ArXiv preprint arXiv:2202.09927 (2022).
@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},
}
- Targeted Hyperparameter Optimization with Lexicographic Preferences Over Multiple Objectives. Shaokun Zhang, Feiran Jia, Chi Wang, Qingyun Wu. ICLR 2023 (notable-top-5%).
@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}
}
- Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference. Chi Wang, Susan Xueqing Liu, Ahmed H. Awadallah. ArXiv preprint arXiv:2303.04673 (2023).
@inproceedings{wang2023EcoOptiGen,
title={Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference},
author={Chi Wang and Susan Xueqing Liu and Ahmed H. Awadallah},
year={2023},
booktitle={ArXiv preprint arXiv:2303.04673},
}