15 citations · 38 across the 12 of their papers we have counts for
11 papers · 1 filter
Cost-Optimal LLM Routing with Limited User Feedback under User Satisfaction Guarantees
Herbert Woisetschläger, Arastun Mammadli, Ryan Zhang +1
Inference costs for large language model (LLM) applications are rapidly growing, driven by surging demand and rising infrastructure cost. Users expect high-quality responses, and i…
MESS+: Dynamically Learned Inference-Time LLM Routing in Model Zoos with Service Level Guarantees
Herbert Woisetschläger, Ryan Zhang, Shiqiang Wang +1
Open-weight large language model (LLM) zoos provide access to numerous high-quality models, but selecting the appropriate model for specific tasks remains challenging and requires…
Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining
Daouda Sow, Herbert Woisetschläger, Saikiran Bulusu +3
Pretraining large language models (LLMs) on vast and heterogeneous datasets is crucial for achieving state-of-the-art performance across diverse downstream tasks. However, current…
MESS+: Energy-Optimal Inferencing in Language Model Zoos with Service Level Guarantees
Ryan Zhang, Herbert Woisetschläger, Shiqiang Wang +1
Open-weight large language model (LLM) zoos allow users to quickly integrate state-of-the-art models into systems. Despite increasing availability, selecting the most appropriate m…
Vertical Federated Learning with Missing Features During Training and Inference
Pedro Valdeira, Shiqiang Wang, Yuejie Chi
Vertical federated learning trains models from feature-partitioned datasets across multiple clients, who collaborate without sharing their local data. Standard approaches assume th…
FADAS: Towards Federated Adaptive Asynchronous Optimization
Yujia Wang, Shiqiang Wang, Songtao Lu +1
Federated learning (FL) has emerged as a widely adopted training paradigm for privacy-preserving machine learning. While the SGD-based FL algorithms have demonstrated considerable…