Merge branch 'main' into 191-add-example-notebook-for-the-core

README.md

@@ -94,4 +94,14 @@ The documentation of ``shapiq`` can be found at https://shapiq.readthedocs.io
 
 ## 💬 Citation
 
-If you **enjoy** `shapiq` consider starring ⭐ the repository.
+If you use the `shapiq` package, please cite our [NeurIPS paper](https://arxiv.org/abs/2410.01649):
+
+```html
+@inproceedings{muschalik2024shapiq,
+  title     = {shapiq: Shapley Interactions for Machine Learning},
+  author    = {Maximilian Muschalik and Hubert Baniecki and Fabian Fumagalli and
+               Patrick Kolpaczki and Barbara Hammer and Eyke H\"{u}llermeier},
+  booktitle = {NeurIPS},
+  year      = {2024}
+}
+```

docs/source/index.rst

@@ -3,6 +3,19 @@ The ``shapiq`` Python package
 
 Shapley Interaction Quantification (``shapiq``) is a Python package for (1) approximating any-order Shapley interactions, (2) benchmarking game-theoretical algorithms for machine learning, (3) explaining feature interactions of model predictions. ``shapiq`` extends the well-known `shap <https://github.com/shap/shap>`_ package for both researchers working on game theory in machine learning, as well as the end-users explaining models. SHAP-IQ extends individual Shapley values by quantifying the **synergy** effect between entities (aka **players** in the jargon of game theory) like explanatory features, data points, or weak learners in ensemble models. Synergies between players give a more comprehensive view of machine learning models.
 
+If you use the ``shapiq`` package, please cite our `NeurIPS paper <https://arxiv.org/abs/2410.01649>`_:
+
+.. code::
+
+   @inproceedings{muschalik2024shapiq,
+      title     = {shapiq: Shapley Interactions for Machine Learning},
+      author    = {Maximilian Muschalik and Hubert Baniecki and Fabian Fumagalli and
+                   Patrick Kolpaczki and Barbara Hammer and Eyke H\"{u}llermeier},
+      booktitle = {NeurIPS},
+      year      = {2024}
+   }
+
+
 Contents
 ~~~~~~~~