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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 51–97 of 97 papers

TitleStatusHype
Fast and Private Submodular and k-Submodular Functions Maximization with Matroid Constraints—0
Understanding collections of related datasets using dependent MMD coresetsCode0
Deuteros 2.0: Peptide-level significance testing of data from hydrogen deuterium exchange mass spectrometryCode0
Non-Adaptive Adaptive Sampling on Turnstile Streams—0
How to Solve Fair k-Center in Massive Data Models—0
Regularized Submodular Maximization at Scale—0
Streaming Submodular Maximization under a k-Set System ConstraintCode0
Fair k-Centers via Maximum Matching—0
How to Solve Fair k-Center in Massive Data Models—0
Streaming Submodular Maximization under a k-Set System Constraint—0
Differentially Private Distributed Data Summarization under Covariate Shift—0
An Empirical and Comparative Analysis of Data Valuation with Scalable Algorithms—0
Fast and Accurate Least-Mean-Squares SolversCode0
apricot: Submodular selection for data summarization in PythonCode0
Submodular Streaming in All its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexity—0
Distributed Maximization of Submodular plus Diversity Functions for Multi-label Feature Selection on Huge Datasets—0
Fair k-Center Clustering for Data SummarizationCode0
Real-Time EEG Classification via Coresets for BCI Applications—0
Iterative Projection and Matching: Finding Structure-preserving Representatives and Its Application to Computer VisionCode0
Fast determinantal point processes via distortion-free intermediate sampling—0
Interpreting Black Box Predictions using Fisher Kernels—0
Network Modeling and Pathway Inference from Incomplete Data ("PathInf")—0
Coverage-Based Designs Improve Sample Mining and Hyper-Parameter OptimizationCode0
Scalable Deletion-Robust Submodular Maximization: Data Summarization with Privacy and Fairness Constraints—0
Data Summarization at Scale: A Two-Stage Submodular Approach—0
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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