Oral
Random Classification Noise does not defeat All Convex Potential Boosters Irrespective of Model Choice
Yishay Mansour · Richard Nock · Robert C. Williamson
Ballroom B
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[ Visit Oral B5 Self/Semi-Supervised Learning and Interpretability / Observing Aspects of NN ]
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Abstract:
A landmark negative result of Long and Servedio has had a considerable impact on research and development in boosting algorithms, around the now famous tagline that "noise defeats all convex boosters". In this paper, we appeal to the half-century+ founding theory of losses for class probability estimation, an extension of Long and Servedio's results and a new general convex booster to demonstrate that the source of their negative result is in fact the model class, linear separators. Losses or algorithms are neither to blame. This leads us to a discussion on an otherwise praised aspect of ML, parameterisation.
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