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Unifying lower bounds on prediction dimension of convex surrogates
The convex consistency dimension of a supervised learning task is the lowest prediction dimension $d$ such that there exists a convex surrogate $L : \mathbb{R}^d \times \mathcal Y \to \mathbb R$ that is consistent for the given task.
Convex Elicitation of Continuous Properties
A property or statistic of a distribution is said to be elicitable if it can be expressed as the minimizer of some loss function in expectation.
Egocentric Height Estimation
We used both traditional computer vision approaches and deep learning in order to determine the visual cues that results in best height estimation.
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