We describe and analyze a new approach for feature ranking in the presence of categorical features with a large number of possible values. It is shown that popular ranking criteria...
We present a unified framework for reasoning about worst-case regret bounds for learning algorithms. This framework is based on the theory of duality of convex functions. It brin...
In this paper we formally analyze the interleaver and code design for QAM-based BICM transmissions using the binary reflected Gray code. We develop analytical bounds on the bit e...
Alex Alvarado, Erik Agrell, Leszek Szczecinski, Ar...
Model selection by the predictive least squares (PLS) principle has been thoroughly studied in the context of regression model selection and autoregressive (AR) model order estima...
— We address the problem of energy efficient sensing by adaptively coordinating the sleep schedules of sensor nodes while guaranteeing that values of sleeping nodes can be recov...