Search Results for author:
Found 7 papers, 1 papers with code
Contrastive Unlearning: A Contrastive Approach to Machine Unlearning
Machine unlearning aims to eliminate the influence of a subset of training samples (i. e., unlearning samples) from a trained model.
Private Semi-supervised Knowledge Transfer for Deep Learning from Noisy Labels
PATE combines an ensemble of "teacher models" trained on sensitive data and transfers the knowledge to a "student" model through the noisy aggregation of teachers' votes for labeling unlabeled public data which the student model will be trained on.
DPAR: Decoupled Graph Neural Networks with Node-Level Differential Privacy
The key idea is to decouple the feature projection and message passing via a DP PageRank algorithm which learns the structure information and uses the top-$K$ neighbors determined by the PageRank for feature aggregation.
Communication Efficient Generalized Tensor Factorization for Decentralized Healthcare Networks
Tensor factorization has been proved as an efficient unsupervised learning approach for health data analysis, especially for computational phenotyping, where the high-dimensional Electronic Health Records (EHRs) with patients' history of medical procedures, medications, diagnosis, lab tests, etc., are converted to meaningful and interpretable medical concepts.
Temporal Network Embedding via Tensor Factorization
Representation learning on static graph-structured data has shown a significant impact on many real-world applications.
Spatio-Temporal Tensor Sketching via Adaptive Sampling
In this paper, we propose SkeTenSmooth, a novel tensor factorization framework that uses adaptive sampling to compress the tensor in a temporally streaming fashion and preserves the underlying global structure.
Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis
We propose DPFact, a privacy-preserving collaborative tensor factorization method for computational phenotyping using EHR.
