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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 51–75 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
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
Non-Adaptive Adaptive Sampling on Turnstile Streams—0
One-Shot Coresets: The Case of k-Clustering—0
On the Usefulness of Synthetic Tabular Data Generation—0
PCA-Guided Quantile Sampling: Preserving Data Structure in Large-Scale Subsampling—0
Operations for Autonomous Spacecraft—0
Real-Time EEG Classification via Coresets for BCI Applications—0
Regularized Submodular Maximization at Scale—0
Robust Approximation Algorithms for Non-monotone k-Submodular Maximization under a Knapsack Constraint—0
Robust Submodular Maximization: A Non-Uniform Partitioning Approach—0
Scalable Deletion-Robust Submodular Maximization: Data Summarization with Privacy and Fairness Constraints—0
Adaptive Sampling for Fast Constrained Maximization of Submodular Function—0
Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach—0
Coresets for Vector Summarization with Applications to Network Graphs—0
Streaming Submodular Maximization under a k-Set System Constraint—0
Data Summarization at Scale: A Two-Stage Submodular Approach—0
Group Equality in Adaptive Submodular MaximizationCode0
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