163 citations · 206 across the 11 of their papers we have counts for
6 papers · 1 filter
StelLA: Subspace Learning in Low-rank Adaptation using Stiefel Manifold
Zhizhong Li, Sina Sajadmanesh, Jingtao Li +1
Low-rank adaptation (LoRA) has been widely adopted as a parameter-efficient technique for fine-tuning large-scale pre-trained models. However, it still lags behind full fine-tuning…
ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees
Sina Sajadmanesh, Daniel Gatica-Perez
Graph Neural Networks (GNNs) have become a popular tool for learning on graphs, but their widespread use raises privacy concerns as graph data can contain personal or sensitive inf…
GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation
Sina Sajadmanesh, Ali Shahin Shamsabadi, Aurélien Bellet +1
In this paper, we study the problem of learning Graph Neural Networks (GNNs) with Differential Privacy (DP). We propose a novel differentially private GNN based on Aggregation Pert…
Locally Private Graph Neural Networks
Sina Sajadmanesh, Daniel Gatica-Perez
Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise…
NPGLM: A Non-Parametric Method for Temporal Link Prediction
Sina Sajadmanesh, Jiawei Zhang, Hamid R. Rabiee
In this paper, we try to solve the problem of temporal link prediction in information networks. This implies predicting the time it takes for a link to appear in the future, given…
A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics
Seyed Ali Osia, Ali Shahin Shamsabadi, Sina Sajadmanesh +5
Internet of Things (IoT) devices and applications are being deployed in our homes and workplaces. These devices often rely on continuous data collection to feed machine learning mo…