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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
An Empirical and Comparative Analysis of Data Valuation with Scalable Algorithms—0
Fast and Accurate Least-Mean-Squares SolversCode0
apricot: Submodular selection for data summarization in PythonCode0
Submodular Streaming in All its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexity—0
Distributed Maximization of Submodular plus Diversity Functions for Multi-label Feature Selection on Huge Datasets—0
Fair k-Center Clustering for Data SummarizationCode0
Real-Time EEG Classification via Coresets for BCI Applications—0
Iterative Projection and Matching: Finding Structure-preserving Representatives and Its Application to Computer VisionCode0
Fast determinantal point processes via distortion-free intermediate sampling—0
Interpreting Black Box Predictions using Fisher Kernels—0
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