Data Summarization
33 papers with code • 0 benchmarks • 2 datasets
Data Summarization is a central problem in the area of machine learning, where we want to compute a small summary of the data.
Benchmarks
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Libraries
Use these libraries to find Data Summarization models and implementations
MentSum
PJM(AEP)
Latest papers
Synthetic Dataset Generation of Driver Telematics
This article describes techniques employed in the production of a synthetic dataset of driver telematics emulated from a similar real insurance dataset.
Sequential estimation of Spearman rank correlation using Hermite series estimators
To treat the non-stationary setting, we introduce a novel, exponentially weighted estimator for the Spearman rank correlation, which allows the local nonparametric correlation of a bivariate data stream to be tracked.
Very Fast Streaming Submodular Function Maximization
Data summarization has become a valuable tool in understanding even terabytes of data.
Semi-supervised Batch Active Learning via Bilevel Optimization
zalanborsos/bilevel_coresets
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Active learning is an effective technique for reducing the labeling cost by improving data efficiency.
Fair and Representative Subset Selection from Data Streams
We study the problem of extracting a small subset of representative items from a large data stream.
$β$-Cores: Robust Large-Scale Bayesian Data Summarization in the Presence of Outliers
Modern machine learning applications should be able to address the intrinsic challenges arising over inference on massive real-world datasets, including scalability and robustness to outliers.
Understanding collections of related datasets using dependent MMD coresets
Understanding how two datasets differ can help us determine whether one dataset under-represents certain sub-populations, and provides insights into how well models will generalize across datasets.
Flexible Dataset Distillation: Learn Labels Instead of Images
In particular, we study the problem of label distillation - creating synthetic labels for a small set of real images, and show it to be more effective than the prior image-based approach to dataset distillation.
Deuteros 2.0: Peptide-level significance testing of data from hydrogen deuterium exchange mass spectrometry
There are currently very few software packages available that offer quick and informative comparison of HDX-MS datasets and even few-er which offer statistical analysis and advanced visualization.
CO-Optimal Transport
Optimal transport (OT) is a powerful geometric and probabilistic tool for finding correspondences and measuring similarity between two distributions.
