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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 31–40 of 97 papers

TitleStatusHype
Analysis of Persian News Agencies on Instagram, A Words Co-occurrence Graph-based Approach—0
GIST: Greedy Independent Set Thresholding for Diverse Data Summarization—0
Federated Combinatorial Multi-Agent Multi-Armed Bandits—0
Fair Clustering for Data Summarization: Improved Approximation Algorithms and Complexity Insights—0
Achieving Long-term Fairness in Submodular Maximization through Randomization—0
Adaptive Sampling for Fast Constrained Maximization of Submodular Function—0
Fair k-Centers via Maximum Matching—0
Coresets for Vector Summarization with Applications to Network Graphs—0
Fast and Private Submodular and k-Submodular Functions Maximization with Matroid Constraints—0
Dynamic Spatio-Temporal Summarization using Information Based Fusion—0
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