Example Applications

LICENSE

@@ -186,7 +186,7 @@
       same "printed page" as the copyright notice for easier
       identification within third-party archives.
 
-   Copyright 2022-2024 the GeometricKernels Contributors
+   Copyright 2022-2025 the GeometricKernels Contributors
 
    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.

README.md

@@ -120,6 +120,22 @@ This should output
  [0.36 0.36 1.  ]]
 ```
 
+## Notebooks
+
+You can find numerous example notebooks for GeometricKernels [here in the docs](https://geometric-kernels.github.io/GeometricKernels/examples/index.html) or [here in the tree](https://github.com/geometric-kernels/GeometricKernels/tree/main/notebooks). These cover every *space* (e.g., sphere, graph), *backend* (e.g., Torch, JAX), *frontend* (e.g., GPyTorch, GPflow), and more.
+
+## Application Examples
+
+Looking for practical use cases of GeometricKernels? Check out
+
+* [PeMS Regression](https://github.com/vabor112/pems-regression):
+  A benchmark suite for graph node regression with uncertainty. This project employs GeometricKernels among other tools, and offers processed data, baseline models, and an [example notebook](https://github.com/vabor112/pems-regression/tree/main/notebooks/GeometricProbabilisticModels.ipynb) for experiments on graph-structured data.
+  Notably, in this benchmark, geometric Gaussian processes built with GeometricKernels have been shown to outperform various alternative methods, including ensembles of graph neural networks and Bayesian graph neural networks.
+
+* [Bayesian optimization demonstration](https://github.com/geometric-kernels/GeometricKernels/blob/main/notebooks/other/Bayesian%20Optimization.ipynb):
+  A minimal notebook illustrating the use of GeometricKernels with the [`botorch`](https://botorch.org/) library for Bayesian optimization.
+  This is a simple, self-contained example designed to demonstrate core concepts rather than to reflect a real-world scenario.
+
 ## Documentation
 
 The documentation for GeometricKernels is available on a [separate website](https://geometric-kernels.github.io/GeometricKernels/index.html).

docs/examples/introduction.rst

@@ -4,4 +4,14 @@ Each of the *Spaces* notebooks explains the basic functionality of the library i
 
 If you are interested in using GeometricKernels with a backend other than NumPy, checkout the *Backends* notebooks.
 
-If you want to use GeometricKernels with one of the popular Gaussian process libraries, checkout the *Frontends* notebooks.
+If you want to use GeometricKernels with one of the popular Gaussian process libraries, checkout the *Frontends* notebooks.
+
+Finally, if you are interested in application examples:
+
+* `PeMS Regression <https://github.com/vabor112/pems-regression>`__  
+    A benchmark suite for graph node regression with uncertainty. This project employs GeometricKernels among other tools, and offers processed data, baseline models, and an `example notebook <https://github.com/vabor112/pems-regression/tree/main/notebooks/GeometricProbabilisticModels.ipynb>`__ for experiments on graph-structured data.  
+    Notably, in this benchmark, geometric Gaussian processes built with GeometricKernels have been shown to outperform various alternative methods, including ensembles of graph neural networks and Bayesian graph neural networks.
+
+* `Bayesian optimization demonstration <https://github.com/geometric-kernels/GeometricKernels/blob/main/notebooks/other/Bayesian%20Optimization.ipynb>`__  
+    A minimal notebook illustrating the use of GeometricKernels with the `botorch <https://botorch.org/>`__ library for Bayesian optimization.  
+    This is a simple, self-contained example designed to demonstrate core concepts rather than to reflect a real-world scenario.

test_requirements.txt

@@ -18,3 +18,4 @@ mypy
 
 # For running some tests
 scikit-learn
+geoopt