— The problem of statistical learning is to construct a predictor of a random variable Y as a function of a related random variable X on the basis of an i.i.d. training sample fr...
We consider signals and operators in finite dimension which have sparse time-frequency representations. As main result we show that an S-sparse Gabor representation in Cn with re...
Optimality conditions are derived for problems of minimizing a general measure of deviation of a random variable, with special attention to situations where the random variable cou...
R. Tyrrell Rockafellar, Stan Uryasev, Michael Zaba...
We present an application of learning-based testing to the problem of automated test case generation (ATCG) for numerical software. Our approach uses n-dimensional polynomial model...
We consider the problem of sequential decision making for random fields corrupted by noise. In this scenario, the decision maker observes a noisy version of the data, yet judged wi...