We present algorithms for a class of resource allocation problems both in the online setting with stochastic input and in the offline setting. This class of problems contains man...
Nikhil R. Devanur, Kamal Jain, Balasubramanian Siv...
The class of algorithms for approximating reasoning tasks presented in this paper is based on approximating the general bucket elimination framework. The algorithms have adjustabl...
Efficient estimation of tail probabilities involving heavy tailed random variables is amongst the most challenging problems in Monte-Carlo simulation. In the last few years, appli...
In this paper, we present a comprehensive theoretical analysis of the sampling technique for the association rule mining problem. Most of the previous works have concentrated only...
Venkatesan T. Chakaravarthy, Vinayaka Pandit, Yogi...
This paper describes a class ofprobabilistic approximation algorithms based on bucket elimination which o er adjustable levels of accuracy ande ciency. We analyzethe approximation...