13 citations · 15 across the 6 of their papers we have counts for
6 papers
Subspace exploration: Bounds on Projected Frequency Estimation
Graham Cormode, Charlie Dickens, David P. Woodruff
Given an dimensional dataset , a projection query specifies a subset of columns which yields a new array. We study the space comple…
Ridge Regression with Frequent Directions: Statistical and Optimization Perspectives
Charlie Dickens
Despite its impressive theory \& practical performance, Frequent Directions (\acrshort{fd}) has not been widely adopted for large-scale regression tasks. Prior work has shown rando…
Interpretable Anomaly Detection with Mondrian P{ó}lya Forests on Data Streams
Charlie Dickens, Eric Meissner, Pablo G. Moreno +1
Anomaly detection at scale is an extremely challenging problem of great practicality. When data is large and high-dimensional, it can be difficult to detect which observations do n…
Similarity of Neural Networks with Gradients
Shuai Tang, Wesley J. Maddox, Charlie Dickens +2
A suitable similarity index for comparing learnt neural networks plays an important role in understanding the behaviour of the highly-nonlinear functions, and can provide insights…
Iterative Hessian Sketch in Input Sparsity Time
Graham Cormode, Charlie Dickens
Scalable algorithms to solve optimization and regression tasks even approximately, are needed to work with large datasets. In this paper we study efficient techniques from matrix s…
Leveraging Well-Conditioned Bases: Streaming \& Distributed Summaries in Minkowski -Norms
Graham Cormode, Charlie Dickens, David P. Woodruff
Work on approximate linear algebra has led to efficient distributed and streaming algorithms for problems such as approximate matrix multiplication, low rank approximation, and reg…