— This paper presents a distributed Maximum A Posteriori (MAP) estimator for multi-robot Cooperative Localization (CL). As opposed to centralized MAP-based CL, the proposed algor...
Esha D. Nerurkar, Stergios I. Roumeliotis, Agostin...
We consider the problem of learning mixtures of arbitrary symmetric distributions. We formulate sufficient separation conditions and present a learning algorithm with provable gua...
Anirban Dasgupta, John E. Hopcroft, Jon M. Kleinbe...
Storing data in distributed systems aims to offer higher bandwidth and scalability than storing locally. But, a couple of disadvantageous issues must be taken into account such as...
Abstract Competition and cooperation can boost the performance of a combinatorial search process. Both can be implemented with a portfolio of algorithms which run in parallel, give...
A powerful approach to search is to try to learn a distribution of good solutions (in particular of the dependencies between their variables) and use this distribution as a basis ...