Monte Carlo methods have been used extensively in the area of stochastic programming. As with other methods that involve a level of uncertainty, theoretical properties are required...
This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop e...
Jie Cheng, Russell Greiner, Jonathan Kelly, David ...
In this paper, we introduce a 2D particle-based approach to achieve realistic water surface behaviors for interactive applications. We formulate 2D particle-based Shallow Water equ...
We present a novel deterministic dependency parsing algorithm that attempts to create the easiest arcs in the dependency structure first in a non-directional manner. Traditional d...
Concurrent program verification is challenging because it involves exploring a large number of possible thread interleavings together with complex sequential reasoning. As a resul...