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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 61–70 of 97 papers

TitleStatusHype
One-Shot Coresets: The Case of k-Clustering—0
On the Usefulness of Synthetic Tabular Data Generation—0
Operations for Autonomous Spacecraft—0
PCA-Guided Quantile Sampling: Preserving Data Structure in Large-Scale Subsampling—0
Regularized Submodular Maximization at Scale—0
Real-Time EEG Classification via Coresets for BCI Applications—0
Robust Approximation Algorithms for Non-monotone k-Submodular Maximization under a Knapsack Constraint—0
Robust Submodular Maximization: A Non-Uniform Partitioning Approach—0
Scalable Deletion-Robust Submodular Maximization: Data Summarization with Privacy and Fairness Constraints—0
Scalable k-Means Clustering via Lightweight Coresets—0
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