Name
Abstract | ||
|---|---|---|
We show that adding differential privacy to Explainable Boosting Machines (EBMs), a recent method for training interpretable ML models, yields state-of-the-art accuracy while protecting privacy. Our experiments on multiple classification and regression datasets show that DP-EBM models suffer surprisingly little accuracy loss even with strong differential privacy guarantees. In addition to high accuracy, two other benefits of applying DP to EBMs are: a) trained models provide exact global and local interpretability, which is often important in settings where differential privacy is needed; and b) the models can be edited after training without loss of privacy to correct errors which DP noise may have introduced. |
Year | Venue | DocType |
|---|---|---|
2021 | INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139 | Conference |
Volume | ISSN | Citations |
139 | 2640-3498 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Harsha Nori | 1 | 0 | 0.34 |
| Rich Caruana | 2 | 4503 | 655.71 |
| Bu, Zhiqi | 3 | 1 | 1.37 |
| Judy Hanwen Shen | 4 | 5 | 1.45 |
| Janardhan Kulkarni | 5 | 28 | 3.34 |