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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 81–90 of 97 papers

TitleStatusHype
Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach—0
Deletion-Robust Submodular Maximization: Data Summarization with "the Right to be Forgotten"—0
Differentially Private Submodular Maximization: Data Summarization in Disguise—0
Subdeterminant Maximization via Nonconvex Relaxations and Anti-concentration—0
Coresets for Vector Summarization with Applications to Network Graphs—0
Robust Submodular Maximization: A Non-Uniform Partitioning Approach—0
Leveraging Sparsity for Efficient Submodular Data Summarization—0
Scalable k-Means Clustering via Lightweight Coresets—0
Graph Summarization Methods and Applications: A Survey—0
Fast Distributed Submodular Cover: Public-Private Data Summarization—0
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