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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 1–50 of 97 papers

TitleStatusHype
Get Rid of Isolation: A Continuous Multi-task Spatio-Temporal Learning FrameworkCode1
Sequential Quantiles via Hermite Series Density EstimationCode1
Soft-Label Dataset Distillation and Text Dataset DistillationCode1
ChartSumm: A Comprehensive Benchmark for Automatic Chart Summarization of Long and Short SummariesCode1
CO-Optimal TransportCode1
Scalability vs. Utility: Do We Have to Sacrifice One for the Other in Data Importance Quantification?Code1
Semi-supervised Batch Active Learning via Bilevel OptimizationCode1
Submodlib: A Submodular Optimization LibraryCode1
Very Fast Streaming Submodular Function MaximizationCode1
Sequential estimation of Spearman rank correlation using Hermite series estimatorsCode1
Flexible Dataset Distillation: Learn Labels Instead of ImagesCode1
Subdeterminant Maximization via Nonconvex Relaxations and Anti-concentration—0
Submodular Streaming in All its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexity—0
Data Summarization beyond Monotonicity: Non-monotone Two-Stage Submodular Maximization—0
Data Summarization via Bilevel Optimization—0
Towards General Robustness to Bad Training Data—0
Deep Submodular Networks for Extractive Data Summarization—0
Deletion-Robust Submodular Maximization: Data Summarization with "the Right to be Forgotten"—0
Analysis of Persian News Agencies on Instagram, A Words Co-occurrence Graph-based Approach—0
Differentiable Submodular Maximization—0
Differentially Private Distributed Data Summarization under Covariate Shift—0
Differentially Private Submodular Maximization: Data Summarization in Disguise—0
A Principled Approach to Data Valuation for Federated Learning—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
Dynamic Non-monotone Submodular Maximization—0
Dynamic Spatio-Temporal Summarization using Information Based Fusion—0
A Unified Framework for Task-Driven Data Quality Management—0
Fair Clustering for Data Summarization: Improved Approximation Algorithms and Complexity Insights—0
An Empirical and Comparative Analysis of Data Valuation with Scalable Algorithms—0
Fair k-Centers via Maximum Matching—0
Fast and Private Submodular and k-Submodular Functions Maximization with Matroid Constraints—0
Fast determinantal point processes via distortion-free intermediate sampling—0
Fast Distributed Submodular Cover: Public-Private Data Summarization—0
Federated Combinatorial Multi-Agent Multi-Armed Bandits—0
GIST: Greedy Independent Set Thresholding for Diverse Data Summarization—0
Graph Summarization Methods and Applications: A Survey—0
GreedyML: A Parallel Algorithm for Maximizing Constrained Submodular Functions—0
Achieving Long-term Fairness in Submodular Maximization through Randomization—0
Group Fairness in Non-monotone Submodular Maximization—0
Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains—0
Guided Exploration of Data Summaries—0
How to be Fair and Diverse?—0
How to Solve Fair k-Center in Massive Data Models—0
How to Solve Fair k-Center in Massive Data Models—0
Interactive Submodular Bandit—0
Interpreting Black Box Predictions using Fisher Kernels—0
Introduction to Core-sets: an Updated Survey—0
AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges—0
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