We study the classic problem of estimating the sum of n variables. The traditional uniform sampling approach requires a linear number of samples to provide any non-trivial guarante...
Suppose we have a large table T of items i, each with a weight wi, e.g., people and their salary. In a general preprocessing step for estimating arbitrary subset sums, we assign e...
Noga Alon, Nick G. Duffield, Carsten Lund, Mikkel ...
We present an nD image processing paradigm to obtain high precision estimates of geometric object properties such as volume, surface area, and length from digitized data. We prove...
The least squares linear regression estimator is well-known to be highly sensitive to unusual observations in the data, and as a result many more robust estimators have been propo...
Abstract. Gaussian process prior systems generally consist of noisy measurements of samples of the putatively Gaussian process of interest, where the samples serve to constrain the...