We examine classes of distributional problems defined in terms of polynomial-time decision algorithms with bounded error probability. The class AvgP [5] has been characterized in...
In this paper, we study online algorithms when the input is not chosen adversarially, but consists of draws from some given probability distribution. While this model has been stu...
Recently there has been a lot of interest in geometrically motivated approaches to data analysis in high dimensional spaces. We consider the case where data is drawn from sampling...
Kuznetsov’s condition says that variables X and Y are independent when any product of bounded functions f(X) and g(Y) behaves in a certain way: the interval of expected values E...
There is evidence that the numbers in probabilistic inference don't really matter. This paper considers the idea that we can make a probabilistic model simpler by making fewe...