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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 76–97 of 97 papers

TitleStatusHype
A Mixed Hierarchical Attention based Encoder-Decoder Approach for Standard Table SummarizationCode0
Differentiable Submodular Maximization—0
Fair and Diverse DPP-based Data SummarizationCode0
An Online Algorithm for Nonparametric CorrelationsCode0
Interactive Submodular Bandit—0
One-Shot Coresets: The Case of k-Clustering—0
Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach—0
Differentially Private Submodular Maximization: Data Summarization in Disguise—0
Deletion-Robust Submodular Maximization: Data Summarization with "the Right to be Forgotten"—0
Subdeterminant Maximization via Nonconvex Relaxations and Anti-concentration—0
Coresets for Vector Summarization with Applications to Network Graphs—0
Robust Submodular Maximization: A Non-Uniform Partitioning Approach—0
Leveraging Sparsity for Efficient Submodular Data Summarization—0
Scalable k-Means Clustering via Lightweight CoresetsCode0
Graph Summarization Methods and Applications: A Survey—0
Fast Distributed Submodular Cover: Public-Private Data Summarization—0
Linear Relaxations for Finding Diverse Elements in Metric Spaces—0
Less is More: Learning Prominent and Diverse Topics for Data Summarization—0
How to be Fair and Diverse?—0
Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains—0
Distributed Submodular Cover: Succinctly Summarizing Massive Data—0
Lazier Than Lazy Greedy—0
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