A basic computational primitive in the analysis of massive datasets is summing simple functions over a large number of objects. Modern applications pose an additional challenge in that such functions often depend on a parameter vector $y$ (query) that is unknown a priori. Given a set of points $X\subset \mathbb{R}^{d}$ and a pairwise function $w:\mathbb{R}^{d}\times \mathbb{R}^{d}\to [0,1]$, we study the problem of designing a data-structure that enables sublinear-time approximation of the summation $Z_{w}(y)=\frac{1}{|X|}\sum_{x\in X}w(x,y)$ for any query $y\in \mathbb{R}^{d}$. By combining ideas from Harmonic Analysis (partitions of unity and approximation theory) with Hashing-Based-Estimators [Charikar, Siminelakis FOCS'17], we provide a general framework for designing such data structures through hashing that reaches far beyond what previous techniques allowed. A key design principle is a collection of $T\geq 1$ hashing schemes with collision probabilities $p_{1},\ldots, p_{T}$ such that $\sup_{t\in [T]}\{p_{t}(x,y)\} = Θ(\sqrt{w(x,y)})$. This leads to a data-structure that approximates $Z_{w}(y)$ using a sub-linear number of samples from each hash family. Using this new framework along with Distance Sensitive Hashing [Aumuller, Christiani, Pagh, Silvestri PODS'18], we show that such a collection can be constructed and evaluated efficiently for any log-convex function $w(x,y)=e^{φ(\langle x,y\rangle)}$ of the inner product on the unit sphere $x,y\in \mathcal{S}^{d-1}$. Our method leads to data structures with sub-linear query time that significantly improve upon random sampling and can be used for Kernel Density or Partition Function Estimation. We provide extensions of our result from the sphere to $\mathbb{R}^{d}$ and from scalar functions to vector functions.