.. image:: pystat.png
    :height: 20
    :alt: Statistique
    :target: http://www.xavierdupre.fr/app/ensae_teaching_cs/helpsphinx/td_2a_notions.html#pour-un-profil-plutot-data-scientist

.. _l-ml2a-autolearning:

Auto-Learning (metalearning)
++++++++++++++++++++++++++++

.. index:: automl, auto learning, automatic machine learning

*(à venir)*

* `Angluin Algorithm <https://web.archive.org/web/20131202232143/http://www.cse.iitk.ac.in/users/chitti/thesis/references/learningRegSetsFromQueriesAndCounterExamples.pdf>`_

*Article de blog*

* `Automating Development and Optimization of Machine Learning Models <https://www.datanami.com/2017/06/12/automating-development-optimization-machine-learning-models/>`_

*Lectures*

* `ATM: A distributed, collaborative, scalable system for automated machine learning <https://cyphe.rs/static/atm.pdf>`_
  (site web: `ATM <https://hdi-dai.lids.mit.edu/projects/atm/>`_)
* `FeatureHub: Towards collaborative data science <https://www.micahsmith.com/files/featurehub-smith.pdf>`_
* `Learning to learn by gradient descent by gradient descent <https://papers.nips.cc/paper/6461-learning-to-learn-by-gradient-descent-by-gradient-descent.pdf>`_
* `Matching Networks for One Shot Learning <https://papers.nips.cc/paper/6385-matching-networks-for-one-shot-learning.pdf>`_
* `Efficient and Robust Automated Machine Learning <http://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning.pdf>`_
  (papier derrière `auto-sklearn <https://github.com/automl/auto-sklearn/>`_)
* `Learning Regular Sets from Queries and Counterexamples <https://web.archive.org/web/20131202232143/http://www.cse.iitk.ac.in/users/chitti/thesis/references/learningRegSetsFromQueriesAndCounterExamples.pdf>`_
* `Neural Architecture Search With Reinforcement Learning <https://openreview.net/forum?id=r1Ue8Hcxg&noteId=r1Ue8Hcxg>`_ (`pdf <https://openreview.net/pdf?id=r1Ue8Hcxg>`_
* `ASlib: A Benchmark Library for Algorithm Selection <https://arxiv.org/abs/1506.02465>`_
* `MacroBase: Prioritizing Attention in Fast Data <https://arxiv.org/pdf/1603.00567.pdf>`_
* `A Bayesian criterion for evaluating the robustness of classification rules in binary data sets <http://www.marc-boulle.fr/publications/GayEtAlAKDM12.pdf>`_
* `Bayesian instance selection for the nearest neighbor rule <http://www.marc-boulle.fr/publications/FerrandizEtAlML10.pdf>`_
* `One Model To Learn Them All <https://arxiv.org/abs/1706.05137>`_
* `DLPaper2Code: Auto-generation of Code from Deep Learning Research Papers <https://arxiv.org/pdf/1711.03543.pdf>`_
* `A Global Optimization Algorithm Worth Using <http://blog.dlib.net/2017/12/a-global-optimization-algorithm-worth.html>`_
  (for optimizing hyperparameters - MaxLIPO+TR) - implements
  `Global optimization of Lipschitz functions <https://arxiv.org/abs/1703.02628>`_
* `Random Search for Hyper-Parameter Optimization <http://www.jmlr.org/papers/v13/bergstra12a.html>`_
* `Reptile: a Scalable Metalearning Algorithm <https://arxiv.org/abs/1803.02999>`_
* `Generating Neural Networks with Neural Networks <https://arxiv.org/abs/1801.01952>`_
* `Rapid Adaptation with Conditionally Shifted Neurons <https://arxiv.org/abs/1712.09926>`_
* `Probabilistic Matrix Factorization for Automated Machine Learning <https://arxiv.org/abs/1705.05355>`_
* `Auto-Keras: Efficient Neural Architecture Search with Network Morphism <https://arxiv.org/abs/1806.10282>`_
* `NNI / Tuners <https://github.com/Microsoft/nni/blob/master/docs/en_US/Builtin_Tuner.md>`_

*Tutoriel*

* `Understanding and diagnosing your machine-learning models <https://github.com/GaelVaroquaux/interpreting_ml_tuto>`_

*POMPD*

* `POMDPs for Dummies <http://www.pomdp.org/tutorial/index.html>`_

*Sites*

* `Machine Learning for Automated Algorithm Design <http://www.ml4aad.org/>`_

*Modules*

* `ATM <https://github.com/HDI-Project/ATM>`_
* `FeatureHub <https://github.com/HDI-Project/FeatureHub>`_
* `REP <https://github.com/yandex/rep>`_
* `TPOT <https://github.com/rhiever/tpot>`_
* `auto-sklearn <https://github.com/automl/auto-sklearn/>`_
* `RoBO <https://github.com/automl/RoBO>`_ (bayésien)
* `POMDPy <https://github.com/pemami4911/POMDPy>`_
* :epkg:`auto-keras` (deep learning)
* `nni <https://github.com/Microsoft/nni>`_, il faut lire la section
  `tuner <https://github.com/Microsoft/nni/blob/master/docs/en_US/Builtin_Tuner.md>`_