Private Boosted Decision Trees via Smooth Re-Weighting

29 January 2022

Papers citing "Private Boosted Decision Trees via Smooth Re-Weighting"

8 / 8 papers shown

Title
Scalable and Provably Accurate Algorithms for Differentially Private Distributed Decision Tree Learning Kai Wang Travis Dick Maria-Florina Balcan FedML 27 5 0 19 Dec 2020
Efficient, Noise-Tolerant, and Private Learning via Boosting Mark Bun M. Carmosino Jessica Sorrell FedML 43 18 0 04 Feb 2020
Privacy-Preserving Gradient Boosting Decision Trees Yue Liu Zhaomin Wu Zeyi Wen Bingsheng He 66 78 0 11 Nov 2019
Diffprivlib: The IBM Differential Privacy Library N. Holohan S. Braghin Pól Mac Aonghusa Killian Levacher SyDa 56 130 0 04 Jul 2019
Interpretable and Differentially Private Predictions Frederik Harder Matthias Bauer Mijung Park FAtt 43 53 0 05 Jun 2019
Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds Mark Bun Thomas Steinke 77 831 0 06 May 2016
Differentially Private Empirical Risk Minimization Kamalika Chaudhuri C. Monteleoni Anand D. Sarwate 111 1,487 0 01 Dec 2009
What Can We Learn Privately? S. Kasiviswanathan Homin K. Lee Kobbi Nissim Sofya Raskhodnikova Adam D. Smith 111 1,465 0 06 Mar 2008