This paper proposes the incremental Bayesian optimization algorithm (iBOA), which modifies standard BOA by removing the population of solutions and using incremental updates of t...
We study the problem of uncertainty in the entries of the Kernel matrix, arising in SVM formulation. Using Chance Constraint Programming and a novel large deviation inequality we ...
We propose low-rank representation (LRR) to segment data drawn from a union of multiple linear (or affine) subspaces. Given a set of data vectors, LRR seeks the lowestrank represe...
We introduce the Spherical Admixture Model (SAM), a Bayesian topic model for arbitrary 2 normalized data. SAM maintains the same hierarchical structure as Latent Dirichlet Allocat...
Joseph Reisinger, Austin Waters, Bryan Silverthorn...
In robotics, recognition of human activity has been used extensively for robot task learning through imitation and demonstration. However, there has not been much work on modeling...
Isabel Serrano Vicente, Ville Kyrki, Danica Kragic...