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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
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
A Unified Framework for Task-Driven Data Quality Management—0
Interactive Submodular Bandit—0
Interpreting Black Box Predictions using Fisher Kernels—0
Introduction to Core-sets: an Updated Survey—0
Submodular Streaming in All its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexity—0
Lazier Than Lazy Greedy—0
Less is More: Learning Prominent and Diverse Topics for Data Summarization—0
Leveraging Sparsity for Efficient Submodular Data Summarization—0
Linear Relaxations for Finding Diverse Elements in Metric Spaces—0
Linear Submodular Maximization with Bandit Feedback—0
LLMSense: Harnessing LLMs for High-level Reasoning Over Spatiotemporal Sensor Traces—0
MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering—0
Max-Min Diversification with Fairness Constraints: Exact and Approximation Algorithms—0
Network Modeling and Pathway Inference from Incomplete Data ("PathInf")—0
NNK-Means: Data summarization using dictionary learning with non-negative kernel regression—0
Non-Adaptive Adaptive Sampling on Turnstile Streams—0
One-Shot Coresets: The Case of k-Clustering—0
On the Usefulness of Synthetic Tabular Data Generation—0
Operations for Autonomous Spacecraft—0
PCA-Guided Quantile Sampling: Preserving Data Structure in Large-Scale Subsampling—0
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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