152 citations · 379 across the 19 of their papers we have counts for
6 papers · 1 filter
IFGAN: Missing Value Imputation using Feature-specific Generative Adversarial Networks
Wei Qiu, Yangsibo Huang, Quanzheng Li
Missing value imputation is a challenging and well-researched topic in data mining. In this paper, we propose IFGAN, a missing value imputation algorithm based on Feature-specific…
MixCon: Adjusting the Separability of Data Representations for Harder Data Recovery
Xiaoxiao Li, Yangsibo Huang, Binghui Peng +2
To address the issue that deep neural networks (DNNs) are vulnerable to model inversion attacks, we design an objective function, which adjusts the separability of the hidden data…
TextHide: Tackling Data Privacy in Language Understanding Tasks
Yangsibo Huang, Zhao Song, Danqi Chen +2
An unsolved challenge in distributed or federated learning is to effectively mitigate privacy risks without slowing down training or reducing accuracy. In this paper, we propose Te…
InstaHide: Instance-hiding Schemes for Private Distributed Learning
Yangsibo Huang, Zhao Song, Kai Li +1
How can multiple distributed entities collaboratively train a shared deep net on their private data while preserving privacy? This paper introduces InstaHide, a simple encryption o…
Deep Learning Based Detection and Localization of Intracranial Aneurysms in Computed Tomography Angiography
Dufan Wu, Daniel Montes, Ziheng Duan +4
Purpose: To develop CADIA, a supervised deep learning model based on a region proposal network coupled with a false-positive reduction module for the detection and localization of…
Privacy-preserving Learning via Deep Net Pruning
Yangsibo Huang, Yushan Su, Sachin Ravi +3
This paper attempts to answer the question whether neural network pruning can be used as a tool to achieve differential privacy without losing much data utility. As a first step to…