Definition: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Input: We develop a worst-case analysis of aggregation of binary classifier ensembles in a transductive setting, for a broad class of losses including but not limited to all convex surrogates. The result is a family of parameter-free ensemble aggregation algorithms, which are as efficient as linear learning and prediction for convex risk minimization but work without any relaxations whatsoever on many nonconvex losses like the 0-1 loss. The prediction algorithms take a familiar form, applying “link functions" to a generalized notion of ensemble margin, but without the assumptions typically made in margin-based learning – all this structure follows from a minimax interpretation of loss minimization.

Minimax Binary Classifier Aggregation with General Losses