10 papers
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…
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…
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…
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…