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PyePAL: Pareto active learning for Python
=========================================

This package implements an active learning approach to efficiently and confidently identify the Pareto front with any regression model that can output a mean and a standard deviation.

It works with any number of objectives, missing data, and is highly customizable.

If you find this code useful for your work, please cite:

- `Jablonka, K. M.; Giriprasad, M. J.; Wang, S.; Smit, B.; Yoo, B. Bias Free Multiobjective Active Learning for Materials Design and Discovery, ChemRxiv 2020 (10.26434/chemrxiv.13200197.v1). <https://chemrxiv.org/articles/preprint/Bias_Free_Multiobjective_Active_Learning_for_Materials_Design_and_Discovery/13200197>`_

- `Zuluaga, M.; Krause, A.; Püschel, M. E-PAL: An Active Learning Approach to the Multi-Objective Optimization Problem. Journal of Machine Learning Research 2016, 17 (104), 1–32. <https://jmlr.csail.mit.edu/papers/volume17/15-047/15-047.pdf>`_



Contents
---------
.. toctree::
   :maxdepth: 3

   getting_started
   background
   tutorials
   api
   developer_notes


Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`