96 citations · 444 across the 23 of their papers we have counts for
10 papers · 1 filter
EigenGame Unloaded: When playing games is better than optimizing
Ian Gemp, Brian McWilliams, Claire Vernade +1
We build on the recently proposed EigenGame that views eigendecomposition as a competitive game. EigenGame's updates are biased if computed using minibatches of data, which hinders…
Preserving Differential Privacy Between Features in Distributed Estimation
Christina Heinze-Deml, Brian McWilliams, Nicolai Meinshausen
Privacy is crucial in many applications of machine learning. Legal, ethical and societal issues restrict the sharing of sensitive data making it difficult to learn from datasets th…
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…
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…
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…