This paper deals with an unusual phenomenon where most machine learning algorithms yield good performance on the training set but systematically worse than random performance on th...
Protecting the integrity of software platforms, especially in unmanaged consumer computing systems is a difficult problem. Attackers may attempt to execute buffer overflow attacks ...
Raghunathan Srinivasan, Vivek Iyer, Amit Kanitkar,...
In this paper we show how the complexity of performing nearest neighbor (NNS) search on a metric space is related to the expansion of the metric space. Given a metric space we loo...
Abstract. Most of the work in Machine Learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem...
We consider the problem of derandomizing random walks in the Euclidean space Rk . We show that for k = 2, and in some cases in higher dimensions, such walks can be simulated in Lo...