Intrinsic Muon provides closed-form linear maximization oracles on multiple Riemannian matrix manifolds for unitarily invariant norms, with convergence rates depending only on manifold dimension or rank.
Riemannian preconditioned lora for fine-tuning foundation mod- els.arXiv preprint arXiv:2402.02347
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RF-EXTRA achieves an exact O(1/K) convergence rate to stationary points on the Stiefel manifold via a contractive joint-error recursion with constant step sizes in decentralized static networks.
LA-LoRA decouples LoRA matrix updates in DPFL settings to improve robustness to privacy noise, delivering up to 16.83% higher accuracy than prior LoRA variants on Swin-B under strict epsilon=1.
citing papers explorer
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Intrinsic Muon: Spectral Optimization on Riemannian Matrix Manifolds
Intrinsic Muon provides closed-form linear maximization oracles on multiple Riemannian matrix manifolds for unitarily invariant norms, with convergence rates depending only on manifold dimension or rank.
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A Retraction-Free EXTRA Method for Decentralized Optimization on the Stiefel Manifold
RF-EXTRA achieves an exact O(1/K) convergence rate to stationary points on the Stiefel manifold via a contractive joint-error recursion with constant step sizes in decentralized static networks.
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Rethinking LoRA for Privacy-Preserving Federated Learning in Large Models
LA-LoRA decouples LoRA matrix updates in DPFL settings to improve robustness to privacy noise, delivering up to 16.83% higher accuracy than prior LoRA variants on Swin-B under strict epsilon=1.