【论文笔记13】Differentially Private Optimal Transport: Application to Domain Adaptation, IJCAI 2019

系列传送

我的论文笔记频道

【Active Learning】
【论文笔记01】Learning Loss for Active Learning, CVPR 2019
【论文笔记02】Active Learning For Convolutional Neural Networks: A Core-Set Approch, ICLR 2018
【论文笔记03】Variational Adversarial Active Learning, ICCV 2019
【论文笔记04】Ranked Batch-Mode Active Learning，ICCV 2016

【Transfer Learning】
【论文笔记05】Active Transfer Learning, IEEE T CIRC SYST VID 2020
【论文笔记06】Domain-Adversarial Training of Neural Networks, JMLR 2016
【论文笔记10】A unified framework of active transfer learning for cross-system recommendation, AI 2017
【论文笔记14】Transfer Learning via Minimizing the Performance Gap Between Domains, NIPS 2019

【Differential Privacy】
【论文笔记07】A Survey on Differentially Private Machine Learning, IEEE CIM 2020
【论文笔记09】Differentially Private Hypothesis Transfer Learning, ECML&PKDD 2018
【论文笔记11】 Deep Domain Adaptation With Differential Privay, IEEE TIFS 2020
【论文笔记12】Differential privacy based on importance weighting, Mach Learn 2013
【论文笔记13】Differentially Private Optimal Transport: Application to Domain Adaptation, IJCAI 2019

【Model inversion attack】
【论文笔记08】Model inversion attacks that exploit confidence information and basic countermeasures, SIGSAC 2015