Genetic algorithms—a class of stochastic population-based optimization techniques—have been widely realized as the effective tools to solve complicated optimization problems ...
We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its B...
In this article we consider the statistical inferences of the unknown parameters of a Weibull distribution when the data are Type-I censored. It is well known that the maximum lik...
The class of graphs where the size of a minimum vertex cover equals that of a maximum matching is known as K¨onig-Egerv´ary graphs. K¨onig-Egerv´ary graphs have been studied ex...
We consider the problem of efficiently learning optimal control policies and value functions over large state spaces in an online setting in which estimates must be available afte...