added recent publications to README

README.md

@@ -10,7 +10,7 @@
 <a href="https://github.com/psf/black"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg"></a>
 
 
-A statistical machine learning toolbox for estimating models, distributions, and functions with context-specific parameters.
+An easy-to-use machine learning toolbox for estimating models, distributions, and functions with context-specific parameters.
 
 Context-specific parameters:
 - Find hidden heterogeneity in data -- are all samples the same?
@@ -66,13 +66,16 @@ Feel free to add your own page(s) by sending a PR or request an improvement by c
   <img src="https://contributors-img.web.app/image?repo=cnellington/contextualized" />
 </a>
 
-ContextualizedML was originally implemented by [Caleb Ellington](https://calebellington.com/) (CMU) and [Ben Lengerich](http://web.mit.edu/~blengeri/www) (MIT).
+Contextualized ML was originally implemented by [Caleb Ellington](https://calebellington.com/) (CMU) and [Ben Lengerich](http://web.mit.edu/~blengeri/www) (MIT).
 
 Many people have helped. Check out [ACKNOWLEDGEMENTS.md](https://github.com/cnellington/Contextualized/blob/main/ACKNOWLEDGEMENTS.md)!
 
 
 
 ## Related Publications and Pre-prints
+- [Contextualized Machine Learning](https://arxiv.org/abs/2310.11340)
+- [Contextualized Networks Reveal Heterogeneous Transcriptomic Regulation in Tumors at Sample-Specific Resolution](https://www.biorxiv.org/content/10.1101/2023.12.01.569658v1)
+- [Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning](https://arxiv.org/abs/2310.07918)
 - [Automated Interpretable Discovery of Heterogeneous Treatment Effectiveness: A COVID-19 Case Study](https://www.sciencedirect.com/science/article/pii/S1532046422001022)
 - [NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters](http://arxiv.org/abs/2111.01104)
 - [Discriminative Subtyping of Lung Cancers from Histopathology Images via Contextual Deep Learning](https://www.medrxiv.org/content/10.1101/2020.06.25.20140053v1.abstract)