1 citations · 2 across the 4 of their papers we have counts for
5 papers
COMED: The Missing Middle Between Routing and Collaboration in Multi-LLM Inference
Norah Alballa, Wenxuan Zhang, Salma Kharrat +4
No single Large Language Model (LLM) is uniformly reliable across queries, motivating multi-model inference systems that either route among models or combine their outputs. However…
Every Call is Precious: Global Optimization of Black-Box Functions with Unknown Lipschitz Constants
Fares Fourati, Salma Kharrat, Vaneet Aggarwal +1
Optimizing expensive, non-convex, black-box Lipschitz continuous functions presents significant challenges, particularly when the Lipschitz constant of the underlying function is u…
ACING: Actor-Critic for Instruction Learning in Black-Box LLMs
Salma Kharrat, Fares Fourati, Marco Canini
The effectiveness of Large Language Models (LLMs) in solving tasks depends significantly on the quality of their instructions, which often require substantial human effort to craft…
Decentralized Personalized Federated Learning
Salma Kharrat, Marco Canini, Samuel Horvath
This work tackles the challenges of data heterogeneity and communication limitations in decentralized federated learning. We focus on creating a collaboration graph that guides eac…
FilFL: Client Filtering for Optimized Client Participation in Federated Learning
Fares Fourati, Salma Kharrat, Vaneet Aggarwal +2
Federated learning, an emerging machine learning paradigm, enables clients to collaboratively train a model without exchanging local data. Clients participating in the training pro…