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cs.LG2026
Model Compression with Exact Budget Constraints via Riemannian Manifolds
Michael Helcig, Dan Alistarh
Assigning one of K options to each of N groups under a total cost budget is a recurring problem in efficient AI, including mixed-precision quantization, non-uniform pruning, and ex…
cs.LG2026
Statistically-Lossless Quantization of Large Language Models
Michael Helcig, Eldar Kurtic, Dan Alistarh
Model quantization has become essential for efficient large language model deployment, yet existing approaches present clear trade-offs: methods such as GPTQ and AWQ achieve practi…
cs.LG2025
FedCCL: Federated Clustered Continual Learning Framework for Privacy-focused Energy Forecasting
Michael A. Helcig, Stefan Nastic
Privacy-preserving distributed model training is crucial for modern machine learning applications, yet existing Federated Learning approaches struggle with heterogeneous data distr…