Tyler Sypherd

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20192023

most citedBeing Properly Improper

2 citations · 5 across the 5 of their papers we have counts for

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Showing 2019Show all

2 papers · 1 filter

cs.LG2019★ 2 cited

A Tunable Loss Function for Robust Classification: Calibration, Landscape, and Generalization

Tyler Sypherd, Mario Diaz, John Kevin Cava +3

We introduce a tunable loss function called $α$ -loss, parameterized by $α \in (0, \infty]$ , which interpolates between the exponential loss ( $α = 1/2$ ), the log-loss ( $α = 1$ ), and the…

cs.LG2019

A Tunable Loss Function for Binary Classification

Tyler Sypherd, Mario Diaz, Lalitha Sankar +1

We present $α$ -loss, $α \in [1, \infty]$ , a tunable loss function for binary classification that bridges log-loss ( $α = 1$ ) and $0$ - $1$ loss ( $α = \infty$ ). We prove that $α$ -loss has a…