We prove an upper bound on the convergence rate of Markov Chain Monte Carlo (MCMC) algorithms for the important special case when the state space can be aggregated into a smaller ...
In recent years, approximation algorithms based on randomized rounding of fractional optimal solutions have been applied to several classes of discrete optimization problems. In t...
We consider the problem of learning a ranking function that maximizes a generalization of the Wilcoxon-Mann-Whitney statistic on the training data. Relying on an -accurate approxim...
Vikas C. Raykar, Ramani Duraiswami, Balaji Krishna...
Variable selection consists in identifying a k-subset of a set of original variables that is optimal for a given criterion of adequate approximation to the whole data set. Several...
Abstract. We propose a BFGS primal-dual interior point method for minimizing a convex function on a convex set defined by equality and inequality constraints. The algorithm generat...
Paul Armand, Jean Charles Gilbert, Sophie Jan-J&ea...