We describe a Markov chain Monte Carlo (MCMC)-based algorithm for sampling solutions to mixed Boolean/integer constraint problems. The focus of this work differs in two points from...
This paper analyzes the potential advantages and theoretical challenges of “active learning” algorithms. Active learning involves sequential sampling procedures that use infor...
Randomization is an efficient tool for global optimization. We here define a method which keeps : – the order 0 of evolutionary algorithms (no gradient) ; – the stochastic as...
— We investigate the problem of inferring the packet loss characteristics of Internet links using server-based measurements. Unlike much of existing work on network tomography th...
A new enhancement of RANSAC, the locally optimized RANSAC (LO-RANSAC), is introduced. It has been observed that, to find an optimal solution (with a given probability), the numbe...