163 citations · 527 across the 28 of their papers we have counts for
5 papers · 1 filter
Efficient Online Bootstrapping for Large Scale Learning
Zhen Qin, Vaclav Petricek, Nikos Karampatziakis +2
Bootstrapping is a useful technique for estimating the uncertainty of a predictor, for example, confidence intervals for prediction. It is typically used on small to moderate sized…
Combining Structured and Unstructured Randomness in Large Scale PCA
Nikos Karampatziakis, Paul Mineiro
Principal Component Analysis (PCA) is a ubiquitous tool with many applications in machine learning including feature construction, subspace embedding, and outlier detection. In thi…
Least Squares Revisited: Scalable Approaches for Multi-class Prediction
Alekh Agarwal, Sham M. Kakade, Nikos Karampatziakis +2
This work provides simple algorithms for multi-class (and multi-label) prediction in settings where both the number of examples n and the data dimension d are relatively large. The…
Discriminative Features via Generalized Eigenvectors
Nikos Karampatziakis, Paul Mineiro
Representing examples in a way that is compatible with the underlying classifier can greatly enhance the performance of a learning system. In this paper we investigate scalable tec…
Loss-Proportional Subsampling for Subsequent ERM
Paul Mineiro, Nikos Karampatziakis
We propose a sampling scheme suitable for reducing a data set prior to selecting a hypothesis with minimum empirical risk. The sampling only considers a subset of the ultimate (unk…