Update paper.md

paper/paper.md

@@ -32,7 +32,7 @@ This lack of interpretability often leads to harmful situations. Models are not
 New tools are being created to support model interpretability. The most known methods are Local Interpretable Model-agnostic Explanations (LIME) [@LIME], SHapley Additive exPlanations (SHAP) [@SHAP] and Descriptive mAchine Learning EXplanations (DALEX) [@DALEX]. General purpose libraries for interpretable Machine Learning in Python are skater [@pramit_choudhary_2018_1198885] and [ELI5](https://eli5.readthedocs.io/en/latest/).
 
 An interesting alternative to these tools is the methodology of *Ceteris Paribus Profiles* and their averages called *Partial Dependency Plots*. They enable to understand how the model response would change if a selected variable is changed. It's a perfect tool for What-If scenarios. _Ceteris Paribus_ is a Latin phrase meaning _all else unchanged_. These plots present the change in model response as the values of one feature change with all others being fixed. Ceteris Paribus method is model-agnostic - it works for any Machine Learning model.
-The idea is an extension of *PDP* (Partial Dependency Plots) [@Friedman00greedyfunction] and *ICE* (Individual Conditional Expectations) plots [@GoldsteinICE]. It allows explaining single observations for multiple variables at the same time.
+The idea is an extension of *PDP* (Partial Dependency Plots) [@friedman2001] and *ICE* (Individual Conditional Expectations) plots [@GoldsteinICE]. It allows explaining single observations for multiple variables at the same time.
 
 In this paper, we introduce a `pyCeterisParibus` library for Python that supports a wide range of tools built on Ceteris Paribus Profiles. There might be several motivations behind utilizing this idea. Imagine a person gets a low credit score. The client wants to understand how to increase the score and the scoring institution (e.g., a bank) should be able to answer such questions. Moreover, this method is useful for researchers and developers to analyze, debug, explain and improve Machine Learning models, assisting the entire process of the model design. The more detailed demonstration is available in the *Examples* section.