Abstract— In this paper, we consider a class of continuoustime, continuous-space stochastic optimal control problems. Building upon recent advances in Markov chain approximation ...
A central issue in relational learning is the choice of an appropriate bias for limiting first-order induction. The purpose of this study is to circumvent this issue within a unifo...
The ICP (Iterative Closest Point) algorithm is widely used for geometric alignment of three-dimensional models when an initial estimate of the relative pose is known. Many variant...
We introduce and study a randomized quasi-Monte Carlo method for estimating the state distribution at each step of a Markov chain. The number of steps in the chain can be random an...
Applications that use collections of very large, distributed datasets have become an increasingly important part of science and engineering. With high performance wide-area networ...