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stat.ML2026
Density-Ratio Losses for Post-Hoc Learning to Defer
Alexander Soen, Ragnar Thobaben, Joakim Jaldén +1
We study post-hoc Learning to Defer (L2D) through the lens of ideal distributions: divergence-regularized reweightings of the data distribution under which a model attains low loss…
cs.LG2026
A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning
Dario Filatrella, Ragnar Thobaben, Mikael Skoglund
We study expected generalization bounds for the Hierarchical Federated Learning (HFL) setup using Wasserstein distance. We introduce a generalized framework in which data is sample…