We study online learnability of a wide class of problems, extending the results of [26] to general notions of performance measure well beyond external regret. Our framework simult...
Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
Classical planning algorithms require that their operators be simple in order for planning to be tractable. However, the complexities of real world domains suggest that, in order ...
In this paper we present a multicost algorithm for the joint time scheduling of the communication and computation resources that will be used by a task. The proposed algorithm sel...
Kostas Christodoulopoulos, Nikolaos D. Doulamis, E...
We start by showing that in an active learning setting, the Perceptron algorithm needs Ω( 1 ε2 ) labels to learn linear separators within generalization error ε. We then prese...
Sanjoy Dasgupta, Adam Tauman Kalai, Claire Montele...
We present two novel perturbation-based linkage learning algorithms that extend LINC [5]; a version of LINC optimised for decomposition tasks (oLINC) and a hierarchical version of...