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https://github.com/mlpapers/automl

Awesome papers on AutoML (Automatic Machine Learning)
https://github.com/mlpapers/automl

List: automl

automatic-machine-learning automl automl-algorithms awesome awesome-list

Last synced: 16 days ago
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Awesome papers on AutoML (Automatic Machine Learning)

Host: GitHub
URL: https://github.com/mlpapers/automl
Owner: mlpapers
Created: 2020-04-04T20:40:37.000Z (over 4 years ago)
Default Branch: master
Last Pushed: 2021-07-20T16:49:19.000Z (over 3 years ago)
Last Synced: 2024-05-22T13:25:28.368Z (7 months ago)
Topics: automatic-machine-learning, automl, automl-algorithms, awesome, awesome-list
Homepage: https://mlpapers.org/automl/
Size: 3.91 KB
Stars: 4
Watchers: 3
Forks: 2
Open Issues: 1
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

ultimate-awesome - automl - Awesome papers on AutoML (Automatic Machine Learning). (Other Lists / Monkey C Lists)

README

        # Automatic Machine Learning (AutoML)

### Hyper-parameters optimization

- Surveys

  - [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://arxiv.org/pdf/2007.15745.pdf) (2020) *Li Yang, Abdallah Shami*

### Neural Architecture Search (NAS)

### Other

- Automatic Statistician

### Software

- **Python**

  - Adanet ([Homepage](https://adanet.readthedocs.io), [PyPI](https://pypi.org/project/adanet/))

  - AgEBO-Tabular ([Homepage](https://github.com/deephyper/NASBigData), [Paper](https://arxiv.org/pdf/2010.16358.pdf))

  - AlphaD3M ([Homepage](https://cims.nyu.edu/~drori/alphad3m.html), [Paper](https://arxiv.org/pdf/1905.10345.pdf)) - closed source. Uses RL to tackle pipeline search

  - Auptimizer ([Homepage](https://lge-arc-advancedai.github.io/auptimizer/), [PyPI](https://pypi.org/project/auptimizer/))

  - Auto-PyTorch ([Homepage](https://github.com/automl/Auto-PyTorch), [Paper](https://arxiv.org/pdf/2006.13799.pdf))

  - Auto-Sklearn ([Homepage](https://automl.github.io/auto-sklearn/master/), [PyPI](https://pypi.org/project/auto-sklearn/)) - based on bayesian optimization

  - Auto-Sklearn 2.0 ([Homepage](https://automl.github.io/auto-sklearn/master/), [PyPI](https://pypi.org/project/auto-sklearn/), [Paper](https://arxiv.org/pdf/2007.04074.pdf)) - uses portfolio learning

  - AutoGBT ([Homepage](https://github.com/flytxtds/AutoGBT))

  - AutoGBT-alt ([Homepage](https://github.com/pfnet-research/autogbt-alt))

  - AutoGluon ([Homepage](https://autogluon.mxnet.io), [PyPI](https://pypi.org/project/autogluon/)) - uses multi-Layer stack ensembling with k-fold ensemble bagging at all layers

  - AutoKeras ([Homepage](https://autokeras.com/), [PyPI](https://pypi.org/project/autokeras/))

  - AutoML Zero ([Homepage](https://github.com/google-research/google-research/tree/master/automl_zero), [Paper](https://arxiv.org/pdf/2003.03384.pdf))

  - AutoML-DSGE ([Homepage](https://github.com/fillassuncao/automl-dsge), [Paper](https://arxiv.org/pdf/2004.00307.pdf))

  - automl-gs ([Homepage](https://github.com/minimaxir/automl-gs/), [PyPI](https://pypi.org/project/automl_gs/)

  - AutoRec ([Homepage](https://github.com/datamllab/AutoRecSys), [Video](https://www.youtube.com/watch?v=z0HkKGVAQkE)) - Automated Recommender System

  - AutoViML([Homepage](https://github.com/AutoViML/Auto_ViML))

  - AX ([Homepage](https://ax.dev/), [PyPI](https://pypi.org/project/ax-platform/))

  - Axolotl ([Homepage](https://gitlab.com/axolotl1/axolotl))

  - BOHB ([Homepage](https://www.automl.org/automl/bohb/))

  - BoTorch ([Homepage](https://botorch.org/docs/introduction.html), [PyPI](https://pypi.org/project/botorch/))

  - Gama ([Homepage](https://github.com/PGijsbers/gama/), [Paper](https://arxiv.org/pdf/2007.04911.pdf))

  - H2O AutoML ([Homepage](http://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html))

  - HpBandSter ([Homepage](https://automl.github.io/HpBandSter/))

  - Hyperopt ([Homepage](http://hyperopt.github.com/hyperopt/), [PyPI](https://pypi.org/project/hyperopt/), [Paper](http://www.coxlab.org/pdfs/2013_bergstra_hyperopt.pdf))

  - Hyperparameter Hunter ([Homepage](https://hyperparameter-hunter.readthedocs.io/en/latest/), [PyPI](https://pypi.org/project/hyperparameter_hunter/))

  - Katib ([Homepage](https://github.com/kubeflow/katib), [Paper](https://arxiv.org/pdf/2006.02085.pdf))

  - Keras Tuner ([Homepage](https://keras-team.github.io/keras-tuner/), [PyPI](https://pypi.org/project/keras-tuner/))

  - Lale ([Homepage](https://github.com/ibm/lale), [PyPI](https://pypi.org/project/lale/), [Paper](https://arxiv.org/pdf/2007.01977.pdf))

  - Ludwig ([Homepage](https://github.com/uber/ludwig/), [PyPI](https://pypi.org/project/ludwig/))

  - Mango ([Homepage](https://github.com/ARM-software/mango), [Paper](https://arxiv.org/pdf/2005.11394.pdf))

  - Milano ([Homepage](https://nvidia.github.io/Milano/))

  - MLBox ([Homepage](https://mlbox.readthedocs.io/en/latest/), [PyPI](https://pypi.org/project/mlbox/))

  - nni ([Homepage](https://nni.readthedocs.io/en/latest/), [PyPI](https://pypi.org/project/nni/))

  - Optuna ([Homepage](https://optuna.org/), [PyPI](https://pypi.org/project/optuna/))

  - Petridish ([Homepage](https://github.com/microsoft/petridishnn), [Paper](https://arxiv.org/abs/1905.13360))

  - PHS ([Homepage](https://github.com/cc-hpc-itwm/PHS), [Paper](https://arxiv.org/pdf/2002.11429))

  - ray ([Homepage](https://ray.io/), [PyPI](https://pypi.org/project/ray/))

  - RECIPE ([Homepage](https://github.com/laic-ufmg/Recipe))

  - ROBO ([Homepage](https://www.automl.org/automl/robo/))

  - SMAC3 ([Homepage](https://automl.github.io/SMAC3/master/), [PyPI](https://pypi.org/project/smac/))

  - Spearmint ([Homepage](https://github.com/HIPS/Spearmint))

  - Talos ([Homepage](https://github.com/autonomio/talos), [PyPI]((https://pypi.org/project/talos/))

  - TPOT ([Homepage](https://automl.info/tpot/), [PyPI](https://pypi.org/project/TPOT/))

  - VolcanoML ([Paper](https://arxiv.org/pdf/2107.08861.pdf), [Code](https://github.com/VolcanoML))

- **Scala**

  - TransmogrifAI ([Homepage](https://github.com/salesforce/TransmogrifAI))