%0 Journal Article %1 fishermodels %A Fisher, Aaron %A Rudin, Cynthia %A Dominici, Francesca %D 2019 %J Journal of Machine Learning Research %K jmlr2019 robustness variable-selection %N 177 %P 1-81 %T All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously %U http://www.jmlr.org/papers/v20/18-760.html %V 20

@article{fishermodels, added-at = {2020-01-13T14:20:42.000+0100}, author = {Fisher, Aaron and Rudin, Cynthia and Dominici, Francesca}, biburl = {https://www.bibsonomy.org/bibtex/2e5920a4e138af5367ba007a33bbe7f4f/kirk86}, description = {All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously}, interhash = {61c8eeb611846bf022109001ebc1d0d7}, intrahash = {e5920a4e138af5367ba007a33bbe7f4f}, issn = {1533-7928}, journal = {Journal of Machine Learning Research}, keywords = {jmlr2019 robustness variable-selection}, number = 177, pages = {1-81}, timestamp = {2020-01-13T14:21:10.000+0100}, title = {All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously}, type = {misc}, url = {http://www.jmlr.org/papers/v20/18-760.html}, volume = 20, year = 2019 }