Abstract Bernard Chazelle Department of Computer Science Princeton University Burton Rosenberg Department of Mathematics and Computer Science Dartmouth College We give a lower bou...
With the increased availability of data for complex domains, it is desirable to learn Bayesian network structures that are sufficiently expressive for generalization while at the ...
Partially observable Markov decision processes (POMDPs) are an intuitive and general way to model sequential decision making problems under uncertainty. Unfortunately, even approx...
Tao Wang, Pascal Poupart, Michael H. Bowling, Dale...
Several analog-to-digital conversion methods for bandlimited signals used in applications, such as quantization schemes, employ coarse quantization coupled with oversampling. The...
We study the worst-case communication complexity of distributed algorithms computing a path problem based on stationary distributions of random walks in a network G with the caveat...