no code implementations • 1 Jan 2022 • Yingqiang Ge, Xiaoting Zhao, Lucia Yu, Saurabh Paul, Diane Hu, Chu-Cheng Hsieh, Yongfeng Zhang
One conspicuous approach is to seek a Pareto efficient solution to guarantee optimal compromises between utility and fairness.
no code implementations • 17 Jun 2015 • Saurabh Paul, Petros Drineas
We introduce single-set spectral sparsification as a deterministic sampling based feature selection technique for regularized least squares classification, which is the classification analogue to ridge regression.
no code implementations • 1 Jun 2014 • Saurabh Paul, Malik Magdon-Ismail, Petros Drineas
In the unsupervised setting, we also provide worst-case guarantees of the radius of the minimum enclosing ball, thereby ensuring comparable generalization as in the full feature space and resolving an open problem posed in Dasgupta et al. We present extensive experiments on real-world datasets to support our theory and to demonstrate that our method is competitive and often better than prior state-of-the-art, for which there are no known provable guarantees.
no code implementations • 26 Nov 2012 • Saurabh Paul, Christos Boutsidis, Malik Magdon-Ismail, Petros Drineas
Let X be a data matrix of rank \rho, whose rows represent n points in d-dimensional space.