Update README.md

README.md

@@ -370,25 +370,32 @@ an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and
 
 **Additionally, `auton-survival` implements the following methodologies:**
 
-[2] [Deep Survival Machines:
-Fully Parametric Survival Regression and
-Representation Learning for Censored Data with Competing Risks.
-IEEE Journal of Biomedical and Health Informatics (2021)](https://arxiv.org/abs/2003.01176)</a>
+[2] [Counterfactual Phenotyping with Censored Time-to-Events (2022)](https://arxiv.org/abs/2202.11089)</a>
 
 ```
-  @article{nagpal2021dsm,
-  title={Deep survival machines: Fully parametric survival regression and representation learning for censored data with competing risks},
-  author={Nagpal, Chirag and Li, Xinyu and Dubrawski, Artur},
-  journal={IEEE Journal of Biomedical and Health Informatics},
-  volume={25},
-  number={8},
-  pages={3163--3175},
+  @article{nagpal2022counterfactual,
+  title={Counterfactual Phenotyping with Censored Time-to-Events},
+  author={Nagpal, Chirag and Goswami, Mononito and Dufendach, Keith and Dubrawski, Artur},
+  journal={arXiv preprint arXiv:2202.11089},
+  year={2022}
+  }
+```
+
+[3] [Deep Cox Mixtures for Survival Regression. Conference on Machine Learning for
+Healthcare (2021)](https://arxiv.org/abs/2101.06536)</a>
+
+```
+  @inproceedings{nagpal2021dcm,
+  title={Deep Cox mixtures for survival regression},
+  author={Nagpal, Chirag and Yadlowsky, Steve and Rostamzadeh, Negar and Heller, Katherine},
+  booktitle={Machine Learning for Healthcare Conference},
+  pages={674--708},
   year={2021},
-  publisher={IEEE}
+  organization={PMLR}
   }
 ```
 
-[3] [Deep Parametric Time-to-Event Regression with Time-Varying Covariates. AAAI
+[4] [Deep Parametric Time-to-Event Regression with Time-Varying Covariates. AAAI
 Spring Symposium (2021)](http://proceedings.mlr.press/v146/nagpal21a.html)</a>
 
 ```
@@ -401,30 +408,27 @@ Spring Symposium (2021)](http://proceedings.mlr.press/v146/nagpal21a.html)</a>
   }
 ```
 
-[4] [Deep Cox Mixtures for Survival Regression. Conference on Machine Learning for
-Healthcare (2021)](https://arxiv.org/abs/2101.06536)</a>
+[5] [Deep Survival Machines:
+Fully Parametric Survival Regression and
+Representation Learning for Censored Data with Competing Risks.
+IEEE Journal of Biomedical and Health Informatics (2021)](https://arxiv.org/abs/2003.01176)</a>
 
 ```
-  @inproceedings{nagpal2021dcm,
-  title={Deep Cox mixtures for survival regression},
-  author={Nagpal, Chirag and Yadlowsky, Steve and Rostamzadeh, Negar and Heller, Katherine},
-  booktitle={Machine Learning for Healthcare Conference},
-  pages={674--708},
+  @article{nagpal2021dsm,
+  title={Deep survival machines: Fully parametric survival regression and representation learning for censored data with competing risks},
+  author={Nagpal, Chirag and Li, Xinyu and Dubrawski, Artur},
+  journal={IEEE Journal of Biomedical and Health Informatics},
+  volume={25},
+  number={8},
+  pages={3163--3175},
   year={2021},
-  organization={PMLR}
+  publisher={IEEE}
   }
 ```
 
-[5] [Counterfactual Phenotyping with Censored Time-to-Events (2022)](https://arxiv.org/abs/2202.11089)</a>
 
-```
-  @article{nagpal2022counterfactual,
-  title={Counterfactual Phenotyping with Censored Time-to-Events},
-  author={Nagpal, Chirag and Goswami, Mononito and Dufendach, Keith and Dubrawski, Artur},
-  journal={arXiv preprint arXiv:2202.11089},
-  year={2022}
-  }
-```
+
+
 
 <a id="install"></a>