We consider linear models for stochastic dynamics. To any such model can be associated a network (namely a directed graph) describing which degrees of freedom interact under the dynamics. We tackle… Expand

We describe a novel approach for computing collision-free global trajectories for p agents with specified initial and final configurations, based on an improved version of the alternating direction method of multipliers (ADMM).Expand

We describe a new instance-based learning algorithm called the Boundary Forest (BF) algorithm, that can be used for supervised and unsupervised learning.Expand

We present a new paradigm for automated document composition based on a generative, unified probabilistic document model (PDM) that models document composition.Expand

Metrics on the space of sets of trajectories are important for scientists in the field of computer vision, machine learning, robotics, and general artificial intelligence.Expand

We provide an exact analytical solution to semi-definite programming and obtain a general and explicit upper bound on the convergence rate of the entire family of over-relaxed ADMM.Expand

When solving consensus optimization problems over a graph, there is often an explicit characterization of the convergence rate of Gradient Descent (GD) using the spectrum of the graph Laplacian. The… Expand

The time to converge to the steady state of a finite Markov chain can be greatly reduced by a lifting operation, as opposed to the lifting of a Markov chains, which sometimes only provides a marginal speedup.Expand