96 citations · 444 across the 23 of their papers we have counts for
4 papers · 1 filter
Learning Representations for Outlier Detection on a Budget
Barbora Micenková, Brian McWilliams, Ira Assent
The problem of detecting a small number of outliers in a large dataset is an important task in many fields from fraud detection to high-energy physics. Two approaches have emerged…
Variance Reduced Stochastic Gradient Descent with Neighbors
Thomas Hofmann, Aurelien Lucchi, Simon Lacoste-Julien +1
Stochastic Gradient Descent (SGD) is a workhorse in machine learning, yet its slow convergence can be a computational bottleneck. Variance reduction techniques such as SAG, SVRG an…
DUAL-LOCO: Distributing Statistical Estimation Using Random Projections
Christina Heinze, Brian McWilliams, Nicolai Meinshausen
We present DUAL-LOCO, a communication-efficient algorithm for distributed statistical estimation. DUAL-LOCO assumes that the data is distributed according to the features rather th…
A Variance Reduced Stochastic Newton Method
Aurelien Lucchi, Brian McWilliams, Thomas Hofmann
Quasi-Newton methods are widely used in practise for convex loss minimization problems. These methods exhibit good empirical performance on a wide variety of tasks and enjoy super-…