We propose XSEED, a synopsis of path queries for cardinality estimation that is accurate, robust, efficient, and adaptive to memory budgets. XSEED starts from a very small kernel,...
A situation where training and test samples follow different input distributions is called covariate shift. Under covariate shift, standard learning methods such as maximum likeli...
Beamformers are spatial filters that pass source signals in particular focused locations while suppressing interference from elsewhere. The widely-used minimum variance adaptive b...
We introduce an algorithm that, given n objects, learns a similarity matrix over all n2 pairs, from crowdsourced data alone. The algorithm samples responses to adaptively chosen t...
Omer Tamuz, Ce Liu, Serge Belongie, Ohad Shamir, A...
Estimating the selectivity of multidimensional range queries over real valued attributes has significant applications in data exploration and database query optimization. In this p...
Dimitrios Gunopulos, George Kollios, Vassilis J. T...