56 citations · 197 across the 22 of their papers we have counts for
11 papers · 1 filter
Theory meets Practice at the Median: a worst case comparison of relative error quantile algorithms
Graham Cormode, Abhinav Mishra, Joseph Ross +1
Estimating the distribution and quantiles of data is a foundational task in data mining and data science. We study algorithms which provide accurate results for extreme quantile qu…
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…
Towards a Theory of Parameterized Streaming Algorithms
Rajesh Chitnis, Graham Cormode
Parameterized complexity attempts to give a more fine-grained analysis of the complexity of problems: instead of measuring the running time as a function of only the input size, we…
Correlation Clustering in Data Streams
Kook Jin Ahn, Graham Cormode, Sudipto Guha +2
Clustering is a fundamental tool for analyzing large data sets. A rich body of work has been devoted to designing data-stream algorithms for the relevant optimization problems such…
Independent Sets in Vertex-Arrival Streams
Graham Cormode, Jacques Dark, Christian Konrad
We consider the classic maximal and maximum independent set problems in three models of graph streams: In the edge-arrival model we see a stream of edges which collectively define…
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…