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LeiZhang 75318206d1 | 2 years ago | |
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code | 2 years ago | |
data | 2 years ago | |
docs | 2 years ago | |
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Latest papers: (All papers are also put in doc/awesome_papers.md)
Want to quickly learn AutoML?想尽快入门自动机器学习?看下面的教程。
Here are some articles on AutoML theory and survey.
Title | Venue | Year | Code | Note |
---|---|---|---|---|
Automated Machine Learning | Springer Book | 2019 | - | |
Neural architecture search: A survey | JMLR | 2019 | - | |
AutonoML: Towards an Integrated Framework for Autonomous Machine Learning | arXiv | 2020 | - | |
Taking human out of learning applications: A survey on automated machine learning | arXiv | 2018 | - | |
AutoML: A Survey of the State-of-the-Art | arXiv | 2019 | - | |
A Survey on Neural Architecture Search | arXiv | 2019 | - | |
A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions | ACM Computing Surveys | 2021 | - | |
On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice | Neurocomputing | 2020 | github |
Title | Venue | Code | Note | Year |
---|---|---|---|---|
BORE: Bayesian Optimization by Density-Ratio Estimation | ICML | GitHub | NOTE | 2021 |
Meta Learning Black-Box Population-Based Optimizers | ArXiv | GitHub | NOTE | 2021 |
Transfer Bayesian Optimization | Note/Blog | GitHub | NOTE | 2021 |
HEBO: Heteroscedastic Evolutionary Bayesian Optimisation | NeurIPS 2020 black-box competition | GitHub | NOTE | 2020 |
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining | NeurIPS | GitHub | NOTE | 2020 |
Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search | NeurIPS | GitHub | NOTE | 2020 |
Scalable Global Optimization via Local Bayesian Optimization | NeurIPS | GitHub | NOTE | 2019 |
Practical Transfer Learning for Bayesian Optimization | ArXiv | - | NOTE | 2018 |
Two-stage transfer surrogate model for automatic hyperparameter optimization | ECML | GitHub | NOTE | 2016 |
If you are interested in contributing, please refer to HERE for instructions in contribution.
[Notes]This Github repo can be used by following the corresponding licenses. I want to emphasis that it may contain some PDFs or thesis, which were downloaded by me and can only be used for academic purposes. The copyrights of these materials are owned by corresponding publishers or organizations. All this are for better adademic research. If any of the authors or publishers have concerns, please contact me to delete or replace them.
AutoML资源整理
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