TPAMI paper unifies Bregman divergences with surrogate losses for learning
FrnkNlsn · x · 2026-07-23
A TPAMI paper by Richard Nock and Frank Nielsen studies Bregman divergences and surrogate losses for learning. The abstract says it connects classification-calibrated surrogates with several common assumptions, derives equivalent forms for convex and non-convex surrogates, and shows shared algorithmic structure across additive regression, logistic loss, and CART-style top-down induction. It also reports experiments on 40 benchmark domains.
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