Trajectory design for high-dimensional systems with nonconvex constraints is a challenging problem considered in this paper. Classical dynamic programming is often employed, but c...
We show that the class of strongly connected graphical models with treewidth at most k can be properly efficiently PAC-learnt with respect to the Kullback-Leibler Divergence. Prev...
Graphical models are useful for capturing interdependencies of statistical variables in various fields. Estimating parameters describing sparse graphical models of stationary mul...
The segmentation of ultrasound images is challenging due to the difficulty of appropriate modeling of their appearance variations including speckle as well as signal dropout. We ...
We present a new machine learning approach to the inverse parametric sequence alignment problem: given as training examples a set of correct pairwise global alignments, find the p...