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cs.LG2026
A Graph Meta-Network for Learning on Kolmogorov-Arnold Networks
Guy Bar-Shalom, Ami Tavory, Itay Evron +3
Weight-space models learn directly from the parameters of neural networks, enabling tasks such as predicting their accuracy on new datasets. Naive methods -- like applying MLPs to…
cs.LG2026
Optimal L2 Regularization in High-dimensional Continual Linear Regression
Gilad Karpel, Edward Moroshko, Ran Levinstein +3
We study generalization in an overparameterized continual linear regression setting, where a model is trained with L2 (isotropic) regularization across a sequence of tasks. We deri…