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update exampls in doc
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.ipynb_checkpoints/SVM-checkpoint.ipynb

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doc/source/example.rst

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:caption: A few links
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:name: rst-link-gallery
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examples/ReHLine_QR
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examples/ReHLine_SVM_FairSVM
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examples/QR.ipynb
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examples/SVM.ipynb
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examples/FairSVM.ipynb
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List of Examples
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----------------
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.. toctree::
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:maxdepth: 0
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examples/ReHLine_QR.ipynb
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examples/ReHLine_SVM_FairSVM.ipynb
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:maxdepth: 1
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examples/QR.ipynb
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examples/SVM.ipynb
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examples/FairSVM.ipynb

doc/source/examples/.ipynb_checkpoints/FairSVM-checkpoint.ipynb

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doc/source/examples/FairSVM.ipynb

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"* $\\mathbf{\\rho} \\in \\mathbb{R}_+^{d_0}$ is a vector of constants that trade-off predictive accuracy and fairness\n",
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"\n",
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"The constraints limit the correlation between the sensitive features and the decision function, ensuring fairness in the predictions.\n",
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"\n",
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"> **Note.** Since the hinge loss is a plq function, and fairness constraints are linear, thus we can solve it by `rehline.plqERM_Ridge`."
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]
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},

doc/source/examples/SVM.ipynb

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doc/source/index.rst

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- Paper: `NeurIPS | 2023 <https://openreview.net/pdf?id=3pEBW2UPAD>`_
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.. - Open Source: `MIT license <https://opensource.org/licenses/MIT>`_
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`ReHLine` is designed to be a computationally efficient
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and practically useful software package for large-scale regularized ERMs.
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The proposed **ReHLine** solver has appealing exhibits appealing properties:
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.. list-table::
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:maxdepth: 2
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:hidden:
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tutorials
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example
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benchmark

rehline/__init__.py

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# Import from internal C++ module
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from ._base import ReHLine_solver, _BaseReHLine, _make_loss_rehline_param
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from ._base import (ReHLine_solver, _BaseReHLine,
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_make_constraint_rehline_param, _make_loss_rehline_param)
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from ._class import ReHLine, plqERM_Ridge
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from ._data import make_fair_classification
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from ._internal import rehline_internal, rehline_result

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