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update doc for research papers

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Qingyun Wu 2023-02-03 16:57:16 -08:00
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notebook/autovw.ipynb
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@ -20,7 +20,7 @@
"\n", "\n",
"In this notebook, we use one real data example (regression task) to showcase AutoVW, which is an online AutoML solution based on the following work:\n", "In this notebook, we use one real data example (regression task) to showcase AutoVW, which is an online AutoML solution based on the following work:\n",
"\n", "\n",
"*ChaCha for online AutoML. Qingyun Wu, Chi Wang, John Langford, Paul Mineiro and Marco Rossi. To appear in ICML 2021.*\n", "*ChaCha for online AutoML. Qingyun Wu, Chi Wang, John Langford, Paul Mineiro and Marco Rossi. ICML 2021.*\n",
"\n", "\n",
"AutoVW is implemented in FLAML. FLAML requires `Python>=3.7`. To run this notebook example, please install:" "AutoVW is implemented in FLAML. FLAML requires `Python>=3.7`. To run this notebook example, please install:"
] ]

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website/docs/Research.md
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@ -19,3 +19,4 @@ For technical details, please check our research publications.
* [ChaCha for Online AutoML](https://www.microsoft.com/en-us/research/publication/chacha-for-online-automl/). Qingyun Wu, Chi Wang, John Langford, Paul Mineiro and Marco Rossi. ICML 2021. * [ChaCha for Online AutoML](https://www.microsoft.com/en-us/research/publication/chacha-for-online-automl/). Qingyun Wu, Chi Wang, John Langford, Paul Mineiro and Marco Rossi. ICML 2021.
* [Fair AutoML](https://arxiv.org/abs/2111.06495). Qingyun Wu, Chi Wang. ArXiv preprint arXiv:2111.06495 (2021). * [Fair AutoML](https://arxiv.org/abs/2111.06495). Qingyun Wu, Chi Wang. ArXiv preprint arXiv:2111.06495 (2021).
* [Mining Robust Default Configurations for Resource-constrained AutoML](https://arxiv.org/abs/2202.09927). Moe Kayali, Chi Wang. ArXiv preprint arXiv:2202.09927 (2022). * [Mining Robust Default Configurations for Resource-constrained AutoML](https://arxiv.org/abs/2202.09927). Moe Kayali, Chi Wang. ArXiv preprint arXiv:2202.09927 (2022).
* [Targeted Hyperparameter Optimization with Lexicographic Preferences Over Multiple Objectives](https://openreview.net/forum?id=0Ij9_q567Ma). Shaokun Zhang, Feiran Jia, Chi Wang, Qingyun Wu. ICLR 2023 (notable-top-5%).