138 citations · 349 across the 10 of their papers we have counts for
12 papers
FedGenSC: Federated Generative Semantic Communication with Channel-Aware Adaptation
Rita Abou Fares, Razan Al Kakoun, Maher Nouiehed +1
Integrating generative adversarial networks (GANs) into federated semantic communication (SemCom) is a natural progression, as generative priors can recover semantic fidelity under…
SEE-OoD: Supervised Exploration For Enhanced Out-of-Distribution Detection
Xiaoyang Song, Wenbo Sun, Maher Nouiehed +2
Current techniques for Out-of-Distribution (OoD) detection predominantly rely on quantifying predictive uncertainty and incorporating model regularization during the training phase…
Rethinking Cost-sensitive Classification in Deep Learning via Adversarial Data Augmentation
Qiyuan Chen, Raed Al Kontar, Maher Nouiehed +2
Cost-sensitive classification is critical in applications where misclassification errors widely vary in cost. However, over-parameterization poses fundamental challenges to the cos…
GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learning
Xubo Yue, Maher Nouiehed, Raed Al Kontar
In this paper we propose \texttt{GIFAIR-FL}: a framework that imposes \textbf{G}roup and \textbf{I}ndividual \textbf{FAIR}ness to \textbf{F}ederated \textbf{L}earning settings. By…
SALR: Sharpness-aware Learning Rate Scheduler for Improved Generalization
Xubo Yue, Maher Nouiehed, Raed Al Kontar
In an effort to improve generalization in deep learning and automate the process of learning rate scheduling, we propose SALR: a sharpness-aware learning rate update technique desi…
Non-convex Min-Max Optimization: Applications, Challenges, and Recent Theoretical Advances
Meisam Razaviyayn, Tianjian Huang, Songtao Lu +3
The min-max optimization problem, also known as the saddle point problem, is a classical optimization problem which is also studied in the context of zero-sum games. Given a class…
Rényi Fair Inference
Sina Baharlouei, Maher Nouiehed, Ahmad Beirami +1
Machine learning algorithms have been increasingly deployed in critical automated decision-making systems that directly affect human lives. When these algorithms are only trained t…
A Trust Region Method for Finding Second-Order Stationarity in Linearly Constrained Non-Convex Optimization
Maher Nouiehed, Meisam Razaviyayn
Motivated by TRACE algorithm [Curtis et al. 2017], we propose a trust region algorithm for finding second order stationary points of a linearly constrained non-convex optimization…
Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods
Maher Nouiehed, Maziar Sanjabi, Tianjian Huang +2
Recent applications that arise in machine learning have surged significant interest in solving min-max saddle point games. This problem has been extensively studied in the convex-c…
Convergence to Second-Order Stationarity for Constrained Non-Convex Optimization
Maher Nouiehed, Jason D. Lee, Meisam Razaviyayn
We consider the problem of finding an approximate second-order stationary point of a constrained non-convex optimization problem. We first show that, unlike the gradient descent me…
Learning Deep Models: Critical Points and Local Openness
Maher Nouiehed, Meisam Razaviyayn
With the increasing popularity of non-convex deep models, developing a unifying theory for studying the optimization problems that arise from training these models becomes very sig…
On the Pervasiveness of Difference-Convexity in Optimization and Statistics
Maher Nouiehed, Jong-Shi Pang, Meisam Razaviyayn
With the increasing interest in applying the methodology of difference-of-convex (dc) optimization to diverse problems in engineering and statistics, this paper establishes the dc…