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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
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
Scalable k-Means Clustering via Lightweight Coresets—0
A Principled Approach to Data Valuation for Federated Learning—0
An Empirical and Comparative Analysis of Data Valuation with Scalable Algorithms—0
Analysis of Persian News Agencies on Instagram, A Words Co-occurrence Graph-based Approach—0
Achieving Long-term Fairness in Submodular Maximization through Randomization—0
Data Summarization beyond Monotonicity: Non-monotone Two-Stage Submodular Maximization—0
Data Summarization via Bilevel Optimization—0
Deep Submodular Networks for Extractive Data Summarization—0
Deletion-Robust Submodular Maximization: Data Summarization with "the Right to be Forgotten"—0
Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach—0
Differentiable Submodular Maximization—0
Differentially Private Distributed Data Summarization under Covariate Shift—0
Differentially Private Submodular Maximization: Data Summarization in Disguise—0
Towards General Robustness to Bad Training Data—0
Distributed Maximization of Submodular plus Diversity Functions for Multi-label Feature Selection on Huge Datasets—0
Distributed Submodular Cover: Succinctly Summarizing Massive Data—0
Dynamic data summarization for hierarchical spatial clustering—0
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