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stat.ML2016★ 6 cited
A Convex Surrogate Operator for General Non-Modular Loss Functions
Jiaqian Yu, Matthew Blaschko
Empirical risk minimization frequently employs convex surrogates to underlying discrete loss functions in order to achieve computational tractability during optimization. However,…
stat.ML2015★ 5 cited
The Lovász Hinge: A Novel Convex Surrogate for Submodular Losses
Jiaqian Yu, Matthew Blaschko
Learning with non-modular losses is an important problem when sets of predictions are made simultaneously. The main tools for constructing convex surrogate loss functions for set p…