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Data Summarization

Data Summarization is a central problem in the area of machine learning, where we want to compute a small summary of the data.

Source: How to Solve Fair k-Center in Massive Data Models

Papers

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Showing 71–80 of 97 papers

TitleStatusHype
Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach—0
Adaptive Sampling for Fast Constrained Maximization of Submodular Function—0
Streaming Submodular Maximization under a k-Set System Constraint—0
Subdeterminant Maximization via Nonconvex Relaxations and Anti-concentration—0
Submodular Streaming in All its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexity—0
Coresets for Vector Summarization with Applications to Network Graphs—0
Group Equality in Adaptive Submodular MaximizationCode0
A Mixed Hierarchical Attention based Encoder-Decoder Approach for Standard Table SummarizationCode0
An Online Algorithm for Nonparametric CorrelationsCode0
Understanding collections of related datasets using dependent MMD coresetsCode0
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