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cs.LG2026
Which Geometry on Which Layer? Measured Sign-Spectral Assignment in Mixed-Optimizer Training
Hiroki Naganuma, Laura Gomezjurado, Mahdi Ghaznavi +5
Modern neural-network training uses mixed recipes, assigning AdamW-like or sign-based updates to embedding and output layers and spectral updates to hidden matrices. These assignme…
cs.LG2026
Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data
Ahmed Mehdi Inane, Vincent Quirion, Gintare Karolina Dziugaite +1
Noise-based certified machine unlearning currently faces a hard ceiling: the noise magnitude required to certify unlearning typically destroys model utility, particularly for large…
cs.LG2026
Orth-Dion: Eliminating Geometric Mismatch in Distributed Low-Rank Spectral Optimization
Tatsuhiro Nakamori, Laura Gomezjurado Gonzalez, Ganesh Talluri +5
Low-rank gradient compression reduces communication in distributed training by representing updates with rank- factors. Dion is a recent method that approximates Muon, a spectra…