added some citations to loss function docstrings

MuyGPyS/optimize/loss.py

@@ -267,7 +267,8 @@ def make_predict_and_loss_fn(self, *args, **kwargs):
 
 lool_fn = LossFn(_lool_fn, make_var_predict_and_loss_fn)
 """
-Leave-one-out likelihood function.
+Leave-one-out likelihood function given in Equation (10) of
+[wood22scalable]_.
 
 Computes leave-one-out likelihood (LOOL) loss of the predicted versus known
 response. Treats multivariate outputs as interchangeable in terms of loss
@@ -297,7 +298,7 @@ def make_predict_and_loss_fn(self, *args, **kwargs):
 
 lool_fn_unscaled = LossFn(_lool_fn_unscaled, make_var_predict_and_loss_fn)
 """
-Leave-one-out likelihood function.
+Unscaled version of the leave-one-out likelihood function.
 
 Computes leave-one-out likelihood (LOOL) loss of the predicted versus known
 response. Treats multivariate outputs as interchangeable in terms of loss
@@ -353,7 +354,8 @@ def make_predict_and_loss_fn(self, *args, **kwargs):
 
 looph_fn = LossFn(_looph_fn, make_var_predict_and_loss_fn)
 """
-Variance-regularized pseudo-Huber loss function.
+Variance-regularized pseudo-Huber loss function given in Equation (8) of
+[mukangango24robust]_.
 
 Computes a smooth approximation to the Huber loss function, similar to
 :func:`pseudo_huber_fn`, with the addition of both a variance scaling and a

README.md

@@ -316,7 +316,7 @@ Finally, open the file `docs/_build/html/index.html` in your browser of choice.
 ## Testing
 
 In order to run tests locally, first `pip` install `MuyGPyS` from source using 
-either the `tests` option.
+the `tests` option.
 All tests in the `tests/` directory are then runnable as python scripts, e.g.
 ```
 $ python tests/kernels.py

docs/resources/references.rst

@@ -11,7 +11,18 @@ References
     Dana. "Star-Galaxy Image Separation with Computationally Efficient
     Gaussian Process Classification." arXiv preprint
     `arXiv:2105.01106 <https://arxiv.org/abs/2105.01106>`_ (2021).
+
 .. [dunton2022fast] Dunton, Alec M., Benjamin W. Priest, and Amanda Muyskens. 
     "Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation 
     and Precomputation." arXiv preprint 
-    `arXiv:2205.10879 <https://arxiv.org/abs/2205.10879>`_ (2022).
+    `arXiv:2205.10879 <https://arxiv.org/abs/2205.10879>`_ (2022).
+
+.. [wood22scalable] Wood, Killian, Alec M. Dunton, Amanda Muyskens, and
+    Benjamin W. Priest. "Scalable Gaussian Process Hyperparameter Optimization
+    via Coverage Regularization." arXiv preprint
+    `arXiv:2209.11280 <https://arxiv.org/abs/2209.11280>`_ (2022).
+
+.. [mukangango24robust] Mukangango, Juliette, Amanda Muyskens, and Benjamin W.
+    Priest. "A Robust Approach to Gaussian Processes Implementation." arXiv
+    preprint
+    `arXiv:2409.11577 <https://arxiv.org/abs/2409.11577>`_ (2024).