ACCURACY, INTERPRETABILITY, AND DIFFERENTIAL PRIVACY VIA EXPLAINABLE BOOSTING

von NORI, HARSHA
Veröffentlicht in International Conference on Machine Learning (38. : 2021 : Online) International Conference on Machine Learning (ICML 2021 ; Part 11 of 16 (2022)

AN ALGORITHMIC FRAMEWORK FOR DIFFERENTIALLY PRIVATE DATA ANALYSIS ON TRUSTED PROCESSORS

von Allen, Joshua
Veröffentlicht in NeurIPS (33. : 2019 : Vancouver, British Columbia) 32nd Conference on Neural Information Processing Systems (NeurIPS 2019) ; Volume 18 of 20 (2020)

Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data

von Chen, Zhi
Veröffentlicht in ECML PKDD (2021 : Online) Machine learning and principles and practice of knowledge discovery in databases ; Part 1 (2021)