We consider a class of learning problems that involve a structured sparsityinducing norm defined as the sum of -norms over groups of variables. Whereas a lot of effort has been pu...
Significant changes in the instance distribution or associated cost function of a learning problem require one to reoptimize a previously-learned classifier to work under new cond...
Chris Bourke, Kun Deng, Stephen D. Scott, Robert E...
Because an agent’s resources dictate what actions it can possibly take, it should plan which resources it holds over time carefully, considering its inherent limitations (such a...
The use of cone beam computed tomography (CBCT) is growing in the clinical arena due to its ability to provide 3-D information during interventions, its high diagnostic quality (su...
This paper exploits recent developments in sparse approximation and compressive sensing to efficiently perform localization in a sensor network. We introduce a Bayesian framework...
Volkan Cevher, Petros Boufounos, Richard G. Barani...