2 papers
cs.LG2026
Revisiting Thinning Methods for Kernel Learning Problems
Blanca Cano-Camarero, Yago R. Aguado-Carrillo-de-Albornoz, Ángela Fernández-Pascual +1
Kernel methods are widely used because of their strong theoretical guarantees and empirical performance. However, their high computational cost limits their applicability to large-…
cs.LG2026
Contrastive-Collapsed Loss for Flexible and Geometrically Optimal Embeddings and Faster Convergence
Blanca Cano-Camarero, Ángela Fernández-Pascual, José R. Dorronsoro
In this work, we introduce CoCo, a loss function aimed at learning normalized and well-structured representations. The proposed loss encourages intra-class collapse and inter-class…