In this paper, we address the scalability issue plaguing graph-based semi-supervised learning via a small number of anchor points which adequately cover the entire point cloud. Cr...
We propose and analyze two strategies to learn over unordered pairs with kernels, and provide a common theoretical framework to compare them. The strategies are related to methods...
In this paper we propose a novel clustering algorithm based on maximizing the mutual information between data points and clusters. Unlike previous methods, we neither assume the d...
We develop a new algorithm, based on EM, for learning the Linear Dynamical System model. Called the method of Approximated Second-Order Statistics (ASOS) our approach achieves dra...
This paper introduces a novel multiagent learning algorithm, Convergence with Model Learning and Safety (or CMLeS in short), which achieves convergence, targeted optimality agains...