Multiple-drawing dynamic Friedman urns with opposite-reinforcement

Synchronization and fluctuation theorems for interacting Friedman urns

Reinforcement Learning with Multiple Demonstrations

Many tasks in robotics can be described as a trajectory that the robot should follow. Unfortunately, specifying the desired trajectory is often a non-trivial task. For example, when asked to describe the trajectory that a helicopter should follow to perform an aerobatic flip, one would have to not only (a) specify a complete trajectory in state space that intuitively corresponds to the aerobati...

Pólya Urns with Immigration at Random Times

We study the number of white balls in a classical Pólya urn model with the additional feature that, at random times, a black ball is added to the urn. The number of draws between these random times are i.i.d. and, under certain moment conditions on the inter-arrival distribution, we characterize the limiting distribution of the (properly scaled) number of white balls as the number of draws goes...

Dynamic Graph Drawing in Visone

In early 2008 a new testing branch of the network analysis software visone has been made publicly available [Baur et al., 2008]. This realease includes the ability to process dynamic networks. A central feature is a dedicated dynamic layout algorithm which we present here from a methological and application oriented point of view. We first describe this algorithm from an algorithmic perspective...

Drawing Dynamic Graphs Without Timeslices

Timeslices are often used to draw and visualize dynamic graphs. While timeslices are a natural way to think about dynamic graphs, they are routinely imposed on continuous data. Often, it is unclear how many timeslices to select: too few timeslices can miss temporal features such as causality or even graph structure while too many timeslices slows the drawing computation. We present a model for ...