{"id":1866,"url":"https://github.com/mlpapers/automl","name":"automl","description":"Awesome papers on AutoML (Automatic Machine Learning)","projects_count":50,"last_synced_at":"2026-07-31T02:00:21.307Z","repository":{"id":55115669,"uuid":"253094641","full_name":"mlpapers/automl","owner":"mlpapers","description":"Awesome papers on AutoML (Automatic Machine Learning)","archived":false,"fork":false,"pushed_at":"2026-02-14T21:37:42.000Z","size":5,"stargazers_count":6,"open_issues_count":1,"forks_count":2,"subscribers_count":1,"default_branch":"master","last_synced_at":"2026-07-12T01:03:15.918Z","etag":null,"topics":["automatic-machine-learning","automl","automl-algorithms","awesome","awesome-list"],"latest_commit_sha":null,"homepage":"https://mlpapers.org/automl/","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mlpapers.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-04-04T20:40:37.000Z","updated_at":"2026-02-14T21:37:46.000Z","dependencies_parsed_at":"2022-08-14T12:30:41.666Z","dependency_job_id":null,"html_url":"https://github.com/mlpapers/automl","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/mlpapers/automl","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mlpapers%2Fautoml","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mlpapers%2Fautoml/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mlpapers%2Fautoml/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mlpapers%2Fautoml/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mlpapers","download_url":"https://codeload.github.com/mlpapers/automl/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mlpapers%2Fautoml/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":36098609,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-07-20T02:08:10.276Z","status":"online","status_checked_at":"2026-07-31T02:00:06.731Z","response_time":112,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"created_at":"2024-01-04T18:15:35.592Z","updated_at":"2026-07-31T02:00:21.307Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["Uncategorized","Related Topics","Software"],"sub_categories":["Uncategorized"],"readme":"# Automatic Machine Learning (AutoML)\n\n### Hyper-parameters optimization\n- Surveys\n  - [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://arxiv.org/pdf/2007.15745.pdf) (2020) *Li Yang, Abdallah Shami*\n### Neural Architecture Search (NAS)\n### Other\n- Automatic Statistician\n\n## Software\n- **Python**\n  - Adanet ([Homepage](https://adanet.readthedocs.io), [PyPI](https://pypi.org/project/adanet/))\n  - AgEBO-Tabular ([Homepage](https://github.com/deephyper/NASBigData), [Paper](https://arxiv.org/pdf/2010.16358.pdf))\n  - 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\n  - Auptimizer ([Homepage](https://lge-arc-advancedai.github.io/auptimizer/), [PyPI](https://pypi.org/project/auptimizer/))\n  - Auto-PyTorch ([Homepage](https://github.com/automl/Auto-PyTorch), [Paper](https://arxiv.org/pdf/2006.13799.pdf))\n  - Auto-Sklearn ([Homepage](https://automl.github.io/auto-sklearn/master/), [PyPI](https://pypi.org/project/auto-sklearn/)) - based on bayesian optimization\n  - 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\n  - AutoGBT ([Homepage](https://github.com/flytxtds/AutoGBT))\n  - AutoGBT-alt ([Homepage](https://github.com/pfnet-research/autogbt-alt))\n  - 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\n  - AutoKeras ([Homepage](https://autokeras.com/), [PyPI](https://pypi.org/project/autokeras/))\n  - AutoML Zero ([Homepage](https://github.com/google-research/google-research/tree/master/automl_zero), [Paper](https://arxiv.org/pdf/2003.03384.pdf))\n  - AutoML-DSGE ([Homepage](https://github.com/fillassuncao/automl-dsge), [Paper](https://arxiv.org/pdf/2004.00307.pdf))\n  - automl-gs ([Homepage](https://github.com/minimaxir/automl-gs/), [PyPI](https://pypi.org/project/automl_gs/)\n  - AutoRec ([Homepage](https://github.com/datamllab/AutoRecSys), [Video](https://www.youtube.com/watch?v=z0HkKGVAQkE)) - Automated Recommender System\n  - AutoViML([Homepage](https://github.com/AutoViML/Auto_ViML))\n  - AX ([Homepage](https://ax.dev/), [PyPI](https://pypi.org/project/ax-platform/))\n  - Axolotl ([Homepage](https://gitlab.com/axolotl1/axolotl))\n  - BOHB ([Homepage](https://www.automl.org/automl/bohb/))\n  - BoTorch ([Homepage](https://botorch.org/docs/introduction.html), [PyPI](https://pypi.org/project/botorch/))\n  - Gama ([Homepage](https://github.com/PGijsbers/gama/), [Paper](https://arxiv.org/pdf/2007.04911.pdf))\n  - H2O AutoML ([Homepage](http://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html))\n  - HpBandSter ([Homepage](https://automl.github.io/HpBandSter/))\n  - Hyperopt ([Homepage](http://hyperopt.github.com/hyperopt/), [PyPI](https://pypi.org/project/hyperopt/), [Paper](http://www.coxlab.org/pdfs/2013_bergstra_hyperopt.pdf))\n  - Hyperparameter Hunter ([Homepage](https://hyperparameter-hunter.readthedocs.io/en/latest/), [PyPI](https://pypi.org/project/hyperparameter_hunter/))\n  - Katib ([Homepage](https://github.com/kubeflow/katib), [Paper](https://arxiv.org/pdf/2006.02085.pdf))\n  - Keras Tuner ([Homepage](https://keras-team.github.io/keras-tuner/), [PyPI](https://pypi.org/project/keras-tuner/))\n  - Lale ([Homepage](https://github.com/ibm/lale), [PyPI](https://pypi.org/project/lale/), [Paper](https://arxiv.org/pdf/2007.01977.pdf))\n  - Ludwig ([Homepage](https://github.com/uber/ludwig/), [PyPI](https://pypi.org/project/ludwig/))\n  - Mango ([Homepage](https://github.com/ARM-software/mango), [Paper](https://arxiv.org/pdf/2005.11394.pdf))\n  - Milano ([Homepage](https://nvidia.github.io/Milano/))\n  - MLBox ([Homepage](https://mlbox.readthedocs.io/en/latest/), [PyPI](https://pypi.org/project/mlbox/))\n  - nni ([Homepage](https://nni.readthedocs.io/en/latest/), [PyPI](https://pypi.org/project/nni/))\n  - Optuna ([Homepage](https://optuna.org/), [PyPI](https://pypi.org/project/optuna/))\n  - Petridish ([Homepage](https://github.com/microsoft/petridishnn), [Paper](https://arxiv.org/abs/1905.13360))\n  - PHS ([Homepage](https://github.com/cc-hpc-itwm/PHS), [Paper](https://arxiv.org/pdf/2002.11429))\n  - ray ([Homepage](https://ray.io/), [PyPI](https://pypi.org/project/ray/))\n  - RECIPE ([Homepage](https://github.com/laic-ufmg/Recipe))\n  - ROBO ([Homepage](https://www.automl.org/automl/robo/))\n  - SMAC3 ([Homepage](https://automl.github.io/SMAC3/master/), [PyPI](https://pypi.org/project/smac/))\n  - Spearmint ([Homepage](https://github.com/HIPS/Spearmint))\n  - Talos ([Homepage](https://github.com/autonomio/talos), [PyPI]((https://pypi.org/project/talos/))\n  - TPOT ([Homepage](https://automl.info/tpot/), [PyPI](https://pypi.org/project/TPOT/))\n  - VolcanoML ([Paper](https://arxiv.org/pdf/2107.08861.pdf), [Code](https://github.com/VolcanoML))\n\n- **Scala**\n  - TransmogrifAI ([Homepage](https://github.com/salesforce/TransmogrifAI))\n\n## Related Topics\n- [Optimization](https://mlpapers.org/optimization/)\n- [Neural Networks](https://mlpapers.org/neural-nets/)\n- [Ensemble Learning](https://mlpapers.org/ensemble-learning/)\n- [Feature Selection](https://mlpapers.org/feature-selection/)\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/mlpapers%2Fautoml/projects"}