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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…
stat.ML2025
A Connection Between Learning to Reject and Bhattacharyya Divergences
Alexander Soen
Learning to reject provide a learning paradigm which allows for our models to abstain from making predictions. One way to learn the rejector is to learn an ideal marginal distribut…
stat.ML2025
Rejection via Learning Density Ratios
Alexander Soen, Hisham Husain, Philip Schulz +1
Classification with rejection emerges as a learning paradigm which allows models to abstain from making predictions. The predominant approach is to alter the supervised learning pi…