2026
Mitigating Error Propagation in Low-Rank Approximation of Large Models via Distribution-Aware Whitening
ICML 2026poster
Low-rank approximation has emerged as a cornerstone technique for model compression and parameter-efficient fine-tuning, enabling substantial reductions in computation and memory without altering model architectures. However, existing approaches often overlook the shifts in feature distributions ind…