If you are using scikit-survival in your scientific research, please cite the following papers:
1. Pölsterl, S., Navab, N., and Katouzian, A., Fast Training of Support Vector Machines for Survival Analysis. Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2015, Porto, Portugal, Lecture Notes in Computer Science, vol. 9285, pp. 243-259 (2015)
BibTeX entry:
@inproceedings{Poelsterl2015ssvm, title = {Fast Training of Support Vector Machines for Survival Analysis}, author = {P{\"o}lsterl, Sebastian and Navab, Nassir and Katouzian, Amin}, booktitle = {Machine Learning and Knowledge Discovery in Databases}, doi = {10.1007/978-3-319-23525-7_15}, editor = {Appice, Annalisa and Rodrigues, Pedro Pereira and Santos Costa, Vítor and Gama, João and Jorge, Alípio and Soares, Carlos}, pages = {243--259}, series = {Lecture Notes in Computer Science}, year = {2015} }
2. Pölsterl, S., Navab, N., and Katouzian, A., An Efficient Training Algorithm for Kernel Survival Support Vector Machines. 4th Workshop on Machine Learning in Life Sciences, 23 September 2016, Riva del Garda, Italy
@inproceedings{Poelsterl2016kernelssvm, title = {{An Efficient Training Algorithm for Kernel Survival Support Vector Machines}}, author = {P{\"o}lsterl, Sebastian and Navab, Nassir and Katouzian, Amin}, booktitle = {3rd Workshop on Machine Learning in Life Sciences}, url = {https://arxiv.org/abs/1611.07054}, month = {September}, year = {2016} }
3. Pölsterl, S., Gupta, P., Wang, L., Conjeti, S., Katouzian, A., and Navab, N., Heterogeneous ensembles for predicting survival of metastatic, castrate-resistant prostate cancer patients. F1000Research, vol. 5, no. 2676 (2016).
@article{Poelsterl2016ensemble, title = {{Heterogeneous Ensembles for Predicting Survival of Metastatic, Castrate-Resistant Prostate Cancer Patients}}, author = {P{\"o}lsterl, Sebastian and Pankaj, Gupta and Wang, Lichao and Conjeti, Sailesh and Katouzian, Amin and Navab, Nassir}, doi = {10.12688/f1000research.8231.1}, journal = {F1000Research}, month = {November}, number = {2676}, volume = {5}, year = {2016} }