Efficient representations and solutions for large decision problems with continuous and discrete variables are among the most important challenges faced by the designers of automa...
Branislav Kveton, Milos Hauskrecht, Carlos Guestri...
A Bayesian ensemble learning method is introduced for unsupervised extraction of dynamic processes from noisy data. The data are assumed to be generated by an unknown nonlinear ma...
Considering a two-way amplify-and-forward (AF) relay network and aiming to simultaneously maximize the two users’ mutual information lower bounds in the presence of channel esti...
We consider distributed algorithms to optimize random access multihop wireless networks in the presence of fading. Since the associated optimization problem is neither convex nor ...
We present a novel, maximum likelihood framework for automatic spike-sorting based on a joint statistical model of action potential waveform shape and inter-spike interval duratio...