23 citations · 48 across the 5 of their papers we have counts for
5 papers
Scalable Adaptive Stochastic Optimization Using Random Projections
Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher +2
Adaptive stochastic gradient methods such as AdaGrad have gained popularity in particular for training deep neural networks. The most commonly used and studied variant maintains a…
Fast and Robust Least Squares Estimation in Corrupted Linear Models
Brian McWilliams, Gabriel Krummenacher, Mario Lucic +1
Subsampling methods have been recently proposed to speed up least squares estimation in large scale settings. However, these algorithms are typically not robust to outliers or corr…
LOCO: Distributing Ridge Regression with Random Projections
Christina Heinze, Brian McWilliams, Nicolai Meinshausen +1
We propose LOCO, an algorithm for large-scale ridge regression which distributes the features across workers on a cluster. Important dependencies between variables are preserved us…
Subspace clustering of high-dimensional data: a predictive approach
Brian McWilliams, Giovanni Montana
In several application domains, high-dimensional observations are collected and then analysed in search for naturally occurring data clusters which might provide further insights a…
Multi-view predictive partitioning in high dimensions
Brian McWilliams, Giovanni Montana
Many modern data mining applications are concerned with the analysis of datasets in which the observations are described by paired high-dimensional vectorial representations or "vi…