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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
Lazier Than Lazy Greedy—0
Less is More: Learning Prominent and Diverse Topics for Data Summarization—0
Leveraging Sparsity for Efficient Submodular Data Summarization—0
Linear Relaxations for Finding Diverse Elements in Metric Spaces—0
Linear Submodular Maximization with Bandit Feedback—0
LLMSense: Harnessing LLMs for High-level Reasoning Over Spatiotemporal Sensor Traces—0
MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering—0
Max-Min Diversification with Fairness Constraints: Exact and Approximation Algorithms—0
Network Modeling and Pathway Inference from Incomplete Data ("PathInf")—0
NNK-Means: Data summarization using dictionary learning with non-negative kernel regression—0
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