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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 26–50 of 97 papers

TitleStatusHype
Dynamic data summarization for hierarchical spatial clustering—0
Dynamic Non-monotone Submodular Maximization—0
Dynamic Spatio-Temporal Summarization using Information Based Fusion—0
A Unified Framework for Task-Driven Data Quality Management—0
Fair Clustering for Data Summarization: Improved Approximation Algorithms and Complexity Insights—0
An Empirical and Comparative Analysis of Data Valuation with Scalable Algorithms—0
Fair k-Centers via Maximum Matching—0
Fast and Private Submodular and k-Submodular Functions Maximization with Matroid Constraints—0
Fast determinantal point processes via distortion-free intermediate sampling—0
Fast Distributed Submodular Cover: Public-Private Data Summarization—0
Federated Combinatorial Multi-Agent Multi-Armed Bandits—0
GIST: Greedy Independent Set Thresholding for Diverse Data Summarization—0
Graph Summarization Methods and Applications: A Survey—0
GreedyML: A Parallel Algorithm for Maximizing Constrained Submodular Functions—0
Achieving Long-term Fairness in Submodular Maximization through Randomization—0
Group Fairness in Non-monotone Submodular Maximization—0
Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains—0
Guided Exploration of Data Summaries—0
How to be Fair and Diverse?—0
How to Solve Fair k-Center in Massive Data Models—0
How to Solve Fair k-Center in Massive Data Models—0
Interactive Submodular Bandit—0
Interpreting Black Box Predictions using Fisher Kernels—0
Introduction to Core-sets: an Updated Survey—0
AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges—0
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