Data warehouses (DWs) can become inconsistent when some dimensional constraints are not satisfied by the dimension instances. In this paper, we present preliminary results about the effects of the violation of partitioning constraints in homogeneous dimension instances over aggregation queries, and in particular over the summarizability property (SUMM) of the DWs. We are interested in finding w...

The purpose of this paper is to discuss empirical risk minimization when the losses are not necessarily bounded and may have a distribution with heavy tails. In such situations usual empirical averages may fail to provide reliable estimates and empirical risk minimization may provide large excess risk. However, some robust mean estimators proposed in the literature may be used to replace empiri...

In this note we study lower bounds on the empirical minimization algorithm. To explain the basic set up of this algorithm, let (Ω, μ) be a probability space and set X to be a random variable taking values in Ω, distributed according to μ. We are interested in the function learning (noiseless) problem, in which one observes n independent random variables X1, . . . , Xn distributed according to μ...