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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
Data Summarization beyond Monotonicity: Non-monotone Two-Stage Submodular Maximization—0
Time-to-Pattern: Information-Theoretic Unsupervised Learning for Scalable Time Series SummarizationCode0
On the Usefulness of Synthetic Tabular Data Generation—0
Achieving Long-term Fairness in Submodular Maximization through Randomization—0
Group Fairness in Non-monotone Submodular Maximization—0
Max-Min Diversification with Fairness Constraints: Exact and Approximation Algorithms—0
MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart DerenderingCode0
Black-box Coreset Variational InferenceCode0
Balancing Utility and Fairness in Submodular Maximization (Technical Report)Code0
Streaming Algorithms for Diversity Maximization with Fairness ConstraintsCode0
Towards Neural Numeric-To-Text Generation From Temporal Personal Health DataCode0
Group Equality in Adaptive Submodular MaximizationCode0
Guided Exploration of Data Summaries—0
Operations for Autonomous Spacecraft—0
NNK-Means: Data summarization using dictionary learning with non-negative kernel regression—0
Towards General Robustness to Bad Training Data—0
Data Summarization via Bilevel Optimization—0
A Unified Framework for Task-Driven Data Quality Management—0
Adaptive Sampling for Fast Constrained Maximization of Submodular Function—0
Synthetic Dataset Generation of Driver TelematicsCode0
Introduction to Core-sets: an Updated Survey—0
Deep Submodular Networks for Extractive Data Summarization—0
Fair and Representative Subset Selection from Data StreamsCode0
A Principled Approach to Data Valuation for Federated Learning—0
β-Cores: Robust Large-Scale Bayesian Data Summarization in the Presence of OutliersCode0
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