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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
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
A Unified Framework for Task-Driven Data Quality Management—0
Interactive Submodular Bandit—0
Interpreting Black Box Predictions using Fisher Kernels—0
Introduction to Core-sets: an Updated Survey—0
Subdeterminant Maximization via Nonconvex Relaxations and Anti-concentration—0
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
AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges—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
Operations for Autonomous Spacecraft—0
PCA-Guided Quantile Sampling: Preserving Data Structure in Large-Scale Subsampling—0
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