4 citations · 9 across the 25 of their papers we have counts for
29 papers
Binary Cyclic Gradient Coding: Exact Recovery and Robustness to Compression
Hassan Ou-azzou, Imad Kissami, El Houcine Bergou
We introduce binary cyclic gradient coding (BCGC), an exact gradient coding scheme for workers and up to stragglers. Its encoding matrix is binary, circulant when $(s+1)\mi…
TACO: Ternary Absolute-max Column-wise One-sparse Optimizer for LLM Fine-Tuning
Jichao Jiang, Cristian McGee, El Houcine Bergou +3
Full-parameter fine-tuning of large language models (LLMs) incurs substantial optimizer state memory overhead, limiting the model sizes that fit on modern GPUs. Existing approaches…
Trust the Direction, Search the Step: Zero-and-First-Order Methods for LLM Fine-Tuning
Cristian McGee, El Houcine Bergou, Aritra Dutta
Step-size selection remains a central challenge in large-scale neural network optimization; conservative steps slow convergence, while aggressive steps can destabilize it. We combi…
Beyond LoRA: Is Sparsity-Induced Adaptation Better?
Elijah Cadenhead, Cristian McGee, Xin Li +2
Low-rank adaptation (LoRA) and its variants provide a memory- and compute-efficient alternative to full fine-tuning of pre-trained models. However, questions remain about the compa…
FairEnergy: Contribution-Based Fairness meets Energy Efficiency in Federated Learning
Ouiame Marnissi, Hajar EL Hammouti, El Houcine Bergou
Federated learning (FL) enables collaborative model training across distributed devices while preserving data privacy. However, balancing energy efficiency and fair participation w…
Just Few States are Enough: Randomized Sparse Feedback for Stability of Dynamical Systems
Zaid Hadach, Hajar El Hammouti, El Houcine Bergou +1
While classical control theory assumes that the controller has access to measurements of the entire state (or output) at every time instant, this paper investigates a setting where…
Stabilizing Policy Gradient Methods via Reward Profiling
Shihab Ahmed, El Houcine Bergou, Aritra Dutta +1
Policy gradient methods, which have been extensively studied in the last decade, offer an effective and efficient framework for reinforcement learning problems. However, their perf…
If You Want to Be Robust, Be Wary of Initialization
Sofiane Ennadir, Johannes F. Lutzeyer, Michalis Vazirgiannis +1
Graph Neural Networks (GNNs) have demonstrated remarkable performance across a spectrum of graph-related tasks, however concerns persist regarding their vulnerability to adversaria…
Where Have All the Kaczmarz Iterates Gone?
El Houcine Bergou, Soumia Boucherouite, Aritra Dutta +2
The randomized Kaczmarz (RK) algorithm is one of the most computationally and memory-efficient iterative algorithms for solving large-scale linear systems. However, practical appli…
AI-based identification and support of at-risk students: A case study of the Moroccan education system
Ismail Elbouknify, Ismail Berrada, Loubna Mekouar +5
Student dropout is a global issue influenced by personal, familial, and academic factors, with varying rates across countries. This paper introduces an AI-driven predictive modelin…
EUREKHA: Enhancing User Representation for Key Hackers Identification in Underground Forums
Abdoul Nasser Hassane Amadou, Anas Motii, Saida Elouardi +1
Underground forums serve as hubs for cybercriminal activities, offering a space for anonymity and evasion of conventional online oversight. In these hidden communities, malicious a…
Energy Efficient Aerial RIS: Phase Shift Optimization and Trajectory Design
Hajar El Hammouti, Adnane Saoud, Asma Ennahkami +1
Reconfigurable Intelligent Surface (RIS) technology has gained significant attention due to its ability to enhance the performance of wireless communication systems. The main advan…
Semantic-Aware Resource Allocation in Constrained Networks with Limited User Participation
Ouiame Marnissi, Hajar EL Hammouti, El Houcine Bergou
Semantic communication has gained attention as a key enabler for intelligent and context-aware communication. However, one of the key challenges of semantic communications is the n…
Joint Probability Selection and Power Allocation for Federated Learning
Ouiame Marnissi, Hajar EL Hammouti, El Houcine Bergou
In this paper, we study the performance of federated learning over wireless networks, where devices with a limited energy budget train a machine learning model. The federated learn…
Age-of-Information in UAV-assisted Networks: a Decentralized Multi-Agent Optimization
Mouhamed Naby Ndiaye, El Houcine Bergou, Hajar El Hammouti
Unmanned aerial vehicles (UAVs) are a highly promising technology with diverse applications in wireless networks. One of their primary uses is the collection of time-sensitive data…
Demystifying the Myths and Legends of Nonconvex Convergence of SGD
Aritra Dutta, El Houcine Bergou, Soumia Boucherouite +3
Stochastic gradient descent (SGD) and its variants are the main workhorses for solving large-scale optimization problems with nonconvex objective functions. Although the convergenc…
Ensemble DNN for Age-of-Information Minimization in UAV-assisted Networks
Mouhamed Naby Ndiaye, El Houcine Bergou, Hajar El Hammouti
This paper addresses the problem of Age-of-Information (AoI) in UAV-assisted networks. Our objective is to minimize the expected AoI across devices by optimizing UAVs' stopping loc…
A Note on Randomized Kaczmarz Algorithm for Solving Doubly-Noisy Linear Systems
El Houcine Bergou, Soumia Boucherouite, Aritra Dutta +2
Large-scale linear systems, , frequently arise in practice and demand effective iterative solvers. Often, these systems are noisy due to operational errors or faulty data-col…
Muti-Agent Proximal Policy Optimization For Data Freshness in UAV-assisted Networks
Mouhamed Naby Ndiaye, El Houcine Bergou, Hajar El Hammouti
Unmanned aerial vehicles (UAVs) are seen as a promising technology to perform a wide range of tasks in wireless communication networks. In this work, we consider the deployment of…
Linear Scalarization for Byzantine-robust learning on non-IID data
Latifa Errami, El Houcine Bergou
In this work we study the problem of Byzantine-robust learning when data among clients is heterogeneous. We focus on poisoning attacks targeting the convergence of SGD. Although th…
Minibatch Stochastic Three Points Method for Unconstrained Smooth Minimization
Soumia Boucherouite, Grigory Malinovsky, Peter Richtárik +1
In this paper, we propose a new zero order optimization method called minibatch stochastic three points (MiSTP) method to solve an unconstrained minimization problem in a setting w…
Age-of-Updates Optimization for UAV-assisted Networks
Mouhamed Naby Ndiaye, El Houcine Bergou, Mounir Ghogho +1
Unmanned aerial vehicles (UAVs) have been proposed as a promising technology to collect data from IoT devices and relay it to the network. In this work, we are interested in scenar…
Personalized Federated Learning with Communication Compression
El Houcine Bergou, Konstantin Burlachenko, Aritra Dutta +1
In contrast to training traditional machine learning (ML) models in data centers, federated learning (FL) trains ML models over local datasets contained on resource-constrained het…
Client Selection in Federated Learning based on Gradients Importance
Ouiame Marnissi, Hajar El Hammouti, El Houcine Bergou
Federated learning (FL) enables multiple devices to collaboratively learn a global model without sharing their personal data. In real-world applications, the different parties are…