Architects use cycle-by-cycle simulation to evaluate design choices and understand tradeoffs and interactions among design parameters. Efficiently exploring exponential-size desig...
Engin Ipek, Sally A. McKee, Rich Caruana, Bronis R...
Group-Lasso estimators, useful in many applications, suffer from lack of meaningful variance estimates for regression coefficients. To overcome such problems, we propose a full Ba...
Sudhir Raman, Thomas J. Fuchs, Peter J. Wild, Edga...
Low-rank matrix decompositions are essential tools in the application of kernel methods to large-scale learning problems. These decompositions have generally been treated as black...
Recent advances in statistical inference and machine learning close the divide between simulation and classical optimization, thereby enabling more rigorous and robust microarchit...
In this paper, we propose a novel semi-supervised speaker identification method that can alleviate the influence of nonstationarity such as session dependent variation, the reco...