This paper studies iterative learning control (ILC) in a multi-agent framework. A group of agents simultaneously and repeatedly perform the same task. The agents improve their perf...
In many real world applications, the number of examples to learn from is plentiful, but we can only obtain limited information on each individual example. We study the possibiliti...
We derive a robust Euclidean embedding procedure based on semidefinite programming that may be used in place of the popular classical multidimensional scaling (cMDS) algorithm. We...
We describe a statistical approach to software debugging in the presence of multiple bugs. Due to sparse sampling issues and complex interaction between program predicates, many g...
Alice X. Zheng, Michael I. Jordan, Ben Liblit, May...
Many unsupervised algorithms for nonlinear dimensionality reduction, such as locally linear embedding (LLE) and Laplacian eigenmaps, are derived from the spectral decompositions o...