8 citations · 25 across the 11 of their papers we have counts for
11 papers
Sharpness-Aware Parameter Selection for Machine Unlearning
Saber Malekmohammadi, Hong kyu Lee, Li Xiong
It often happens that some sensitive personal information, such as credit card numbers or passwords, are mistakenly incorporated in the training of machine learning models and need…
LoRA Provides Differential Privacy by Design via Random Sketching
Saber Malekmohammadi, Golnoosh Farnadi
Low-rank adaptation of language models has been proposed to reduce the computational and memory overhead of fine-tuning pre-trained language models. LoRA incorporates trainable low…
Semi-Variance Reduction for Fair Federated Learning
Saber Malekmohammadi, Yaoliang Yu
Ensuring fairness in a Federated Learning (FL) system, i.e., a satisfactory performance for all of the participating diverse clients, is an important and challenging problem. There…
Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning
Saber Malekmohammadi, Yaoliang Yu, Yang Cao
High utility and rigorous data privacy are of the main goals of a federated learning (FL) system, which learns a model from the data distributed among some clients. The latter has…
Differentially Private Clustered Federated Learning
Saber Malekmohammadi, Afaf Taik, Golnoosh Farnadi
Federated learning (FL), which is a decentralized machine learning (ML) approach, often incorporates differential privacy (DP) to provide rigorous data privacy guarantees. Previous…
Proportional Fairness in Federated Learning
Guojun Zhang, Saber Malekmohammadi, Xi Chen +1
With the increasingly broad deployment of federated learning (FL) systems in the real world, it is critical but challenging to ensure fairness in FL, i.e. reasonably satisfactory p…
An Operator Splitting View of Federated Learning
Saber Malekmohammadi, Kiarash Shaloudegi, Zeou Hu +1
Over the past few years, the federated learning () community has witnessed a proliferation of new algorithms. However, our understating of the theory of…
PePScenes: A Novel Dataset and Baseline for Pedestrian Action Prediction in 3D
Amir Rasouli, Tiffany Yau, Peter Lakner +3
Predicting the behavior of road users, particularly pedestrians, is vital for safe motion planning in the context of autonomous driving systems. Traditionally, pedestrian behavior…
Graph-SIM: A Graph-based Spatiotemporal Interaction Modelling for Pedestrian Action Prediction
Tiffany Yau, Saber Malekmohammadi, Amir Rasouli +3
One of the most crucial yet challenging tasks for autonomous vehicles in urban environments is predicting the future behaviour of nearby pedestrians, especially at points of crossi…
Non-Parametric Graph Learning for Bayesian Graph Neural Networks
Soumyasundar Pal, Saber Malekmohammadi, Florence Regol +3
Graphs are ubiquitous in modelling relational structures. Recent endeavours in machine learning for graph-structured data have led to many architectures and learning algorithms. Ho…
Sparsity Promoting Reconstruction of Delta Modulated Voice Samples by Sequential Adaptive Thresholds
Mahdi Boloursaz Mashhadi, Saber Malekmohammadi, Farokh Marvasti
In this paper, we propose the family of Iterative Methods with Adaptive Thresholding (IMAT) for sparsity promoting reconstruction of Delta Modulated (DM) voice signals. We suggest…