We study the problem of learning kernel machines transductively for structured output variables. Transductive learning can be reduced to combinatorial optimization problems over a...
Decision-theoretic optimization is becoming a popular tool in the user interface community, but creating accurate cost (or utility) functions has become a bottleneck — in most c...
This paper approaches statistical optimization by examining gate delay variation models and optimization objectives. Most previous work on statistical optimization has focused exc...
Matthew R. Guthaus, Natesan Venkateswaran, Vladimi...
In this paper, we propose to apply sparse canonical correlation analysis (sparse CCA) to an important genome-wide association study problem, eQTL mapping. Existing sparse CCA mode...
In this paper, we present a study on the performance of TCP, in terms of both throughput and energy consumption, in the presence of a Wideband CDMA radio interface typical of thir...