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. We present a new tool based on property elicitation, $d$-flats, for lower-bounding convex consistency dimension.

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. We present a new tool based on property elicitation, d -flats, for lower-bounding convex consistency dimension. We use d -flats to obtain a new lower bound on the convex consistency dimension of risk measures, resolving an open question due to Frongillo and Kash (NeurIPS 2015). In discrete prediction settings, we show that the d -flats approach recovers and even tightens previous lower bounds using feasible subspace dimension.

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. We present a new tool based on property elicitation, d -flats, for lower-bounding convex consistency dimension. We use d -flats to obtain a new lower bound on the convex consistency dimension of risk measures, resolving an open question due to Frongillo and Kash (NeurIPS 2015). In discrete prediction settings, we show that the d -flats approach recovers and even tightens previous lower bounds using feasible subspace dimension.