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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
Group Equality in Adaptive Submodular MaximizationCode0
Iterative Projection and Matching: Finding Structure-preserving Representatives and Its Application to Computer VisionCode0
MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart DerenderingCode0
Scalable k-Means Clustering via Lightweight CoresetsCode0
Streaming Submodular Maximization under a k-Set System ConstraintCode0
Fair and Representative Subset Selection from Data StreamsCode0
Synthetic Dataset Generation of Driver TelematicsCode0
Time-to-Pattern: Information-Theoretic Unsupervised Learning for Scalable Time Series SummarizationCode0
Towards Neural Numeric-To-Text Generation From Temporal Personal Health DataCode0
Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach—0
Fair Clustering for Data Summarization: Improved Approximation Algorithms and Complexity Insights—0
Towards General Robustness to Bad Training Data—0
Fair k-Centers via Maximum Matching—0
Streaming Submodular Maximization under a k-Set System Constraint—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
Data Summarization at Scale: A Two-Stage Submodular Approach—0
Coresets for Vector Summarization with Applications to Network Graphs—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
Adaptive Sampling for Fast Constrained Maximization of Submodular Function—0
Group Fairness in Non-monotone Submodular Maximization—0
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