In this paper, we consider a coded transmission over a frequency selective channel. We propose to study analytically the convergence of the turbo-detector using a maximum a poster...
This paper proposes a novel Bayesian approximation inference method for mixture modeling. Our key idea is to factorize marginal log-likelihood using a variational distribution ove...
We investigate balls-and-bins processes where m weighted balls are placed into n bins using the "power of two choices" paradigm, whereby a ball is inserted into the less...
Abstract. Most of the work in machine learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem...
Statistical selection procedures can identify the best of a finite set of alternatives, where “best” is defined in terms of the unknown expected value of each alternative’...