This paper describes a method of supervised learning based on forward selection branching. This method improves fault tolerance by means of combining information related to general...
Various stochastic programmingproblemscan be formulated as problems of optimization of an expected value function. Quite often the corresponding expectation function cannot be com...
We present a novel method for approximate inference in Bayesian models and regularized risk functionals. It is based on the propagation of mean and variance derived from the Lapla...
Alexander J. Smola, Vishy Vishwanathan, Eleazar Es...
We use convex relaxation techniques to produce lower bounds on the optimal value of subset selection problems and generate good approximate solutions. We then explicitly bound the...
Francis Bach, Selin Damla Ahipasaoglu, Alexandre d...
In this note we present an approximation algorithm for MAX 2SAT that given a (1 - ) satisfiable instance finds an assignment of variables satisfying a 1 - O( ) fraction of all co...
Moses Charikar, Konstantin Makarychev, Yury Makary...