refs.bib

/* HPO */

@article{bergstra2011algorithms,
  title={Algorithms for hyper-parameter optimization},
  author={Bergstra, James and Bardenet, R{\'e}mi and Bengio, Yoshua and K{\'e}gl, Bal{\'a}zs},
  journal={Advances in neural information processing systems},
  volume={24},
  year={2011}
}

@inproceedings{li2018metis,
  title={Metis: Robustly tuning tail latencies of cloud systems},
  author={Li, Zhao Lucis and Liang, Chieh-Jan Mike and He, Wenjia and Zhu, Lianjie and Dai, Wenjun and Jiang, Jin and Sun, Guangzhong},
  booktitle={2018 USENIX Annual Technical Conference (USENIX ATC 18)},
  pages={981--992},
  year={2018}
}

@inproceedings{hutter2011sequential,
  title={Sequential model-based optimization for general algorithm configuration},
  author={Hutter, Frank and Hoos, Holger H and Leyton-Brown, Kevin},
  booktitle={International conference on learning and intelligent optimization},
  pages={507--523},
  year={2011},
  organization={Springer}
}

@article{li2017hyperband,
  title={Hyperband: A novel bandit-based approach to hyperparameter optimization},
  author={Li, Lisha and Jamieson, Kevin and DeSalvo, Giulia and Rostamizadeh, Afshin and Talwalkar, Ameet},
  journal={The Journal of Machine Learning Research},
  volume={18},
  number={1},
  pages={6765--6816},
  year={2017},
  publisher={JMLR. org}
}

@inproceedings{falkner2018bohb,
  title={BOHB: Robust and efficient hyperparameter optimization at scale},
  author={Falkner, Stefan and Klein, Aaron and Hutter, Frank},
  booktitle={International Conference on Machine Learning},
  pages={1437--1446},
  year={2018},
  organization={PMLR}
}

/* NAS */

@inproceedings{zoph2017neural,
  title={Neural Architecture Search with Reinforcement Learning},
  author={Zoph, Barret and Le, Quoc V},
  booktitle={International Conference on Learning Representations},
  year={2017}
}

@inproceedings{zoph2018learning,
  title={Learning transferable architectures for scalable image recognition},
  author={Zoph, Barret and Vasudevan, Vijay and Shlens, Jonathon and Le, Quoc V},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={8697--8710},
  year={2018}
}

@inproceedings{liu2018darts,
  title={DARTS: Differentiable Architecture Search},
  author={Liu, Hanxiao and Simonyan, Karen and Yang, Yiming},
  booktitle={International Conference on Learning Representations},
  year={2018}
}

@inproceedings{cai2018proxylessnas,
  title={ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware},
  author={Cai, Han and Zhu, Ligeng and Han, Song},
  booktitle={International Conference on Learning Representations},
  year={2018}
}

@inproceedings{xie2018snas,
  title={SNAS: stochastic neural architecture search},
  author={Xie, Sirui and Zheng, Hehui and Liu, Chunxiao and Lin, Liang},
  booktitle={International Conference on Learning Representations},
  year={2018}
}

@inproceedings{pham2018efficient,
  title={Efficient neural architecture search via parameters sharing},
  author={Pham, Hieu and Guan, Melody and Zoph, Barret and Le, Quoc and Dean, Jeff},
  booktitle={International conference on machine learning},
  pages={4095--4104},
  year={2018},
  organization={PMLR}
}

@inproceedings{radosavovic2019network,
  title={On network design spaces for visual recognition},
  author={Radosavovic, Ilija and Johnson, Justin and Xie, Saining and Lo, Wan-Yen and Doll{\'a}r, Piotr},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={1882--1890},
  year={2019}
}

@inproceedings{ying2019bench,
  title={Nas-bench-101: Towards reproducible neural architecture search},
  author={Ying, Chris and Klein, Aaron and Christiansen, Eric and Real, Esteban and Murphy, Kevin and Hutter, Frank},
  booktitle={International Conference on Machine Learning},
  pages={7105--7114},
  year={2019},
  organization={PMLR}
}

@inproceedings{dong2019bench,
  title={NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search},
  author={Dong, Xuanyi and Yang, Yi},
  booktitle={International Conference on Learning Representations},
  year={2019}
}