Coreset Selection

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5 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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COREKG: Coreset-Guided Personalized Summarization of Knowledge Graphs
May 14, 2026Sohel Aman Khan, Raghava Mutharaju, Supratim ShitCoreset Selection Knowledge Graphs
Department of CSE, IIIT-Delhi, India · Mehta Family School of Data Science and AI, IIT Palakkad, India
CoRDS: Coreset-based Representative and Diverse Selection for Streaming Video Understanding
May 14, 2026Ailar Mahdizadeh, Puria Azadi, Muchen Li +2Streaming Video Understanding KV Caching
University of British Columbia · Vector Institute
State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives
May 13, 2026Hoang-Son Tran, Pranav Gupta, Rémi Bardenet +1Determinantal Point Process Wavelet Methods
Department of Mathematics National University of Singapore · Univ. Lille, CNRS, Centrale Lille UMR 9189 – CRIStAL
CAST: Collapse-Aware multi-Scale Topology Fusion for Multimodal Coreset Selection
May 12, 2026Boran Zhao, Hetian Liu, Zhenxian Hu +3Data Selection Distribution Matching
School of Software Engineering Xi’an Jiaotong University · the National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University
Nearly Optimal Attention Coresets
May 7, 2026Edo Liberty, Alexandr Andoni, Eldar KleinerSoftmax Attention Efficient Attention
GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning
May 2, 2026Chuang Liu, Zelin Yao, Xueqi Ma +4Graph Representation Learning Self-Supervised Pre-Training
Sangfor Technologies Inc. · Wuhan University · The University of Melbourne +2
Nearly Optimal Subdata Selection
Apr 27, 2026Min Yang, Wei Zheng, John Stufken +3Data Selection Training Data Selection
Department of Mathematics, Statistics, and Computer Science, University of Illinois at Chicago · Department of Business Analytics and Statistics, University of Tennessee · Department of Statistics, George Mason University[stat.ME] +3
Rethinking Dataset Distillation: Hard Truths about Soft Labels
Apr 20, 2026Priyam Dey, Aditya Sahdev, Sunny Bhati +2Training Data Selection Dataset Distillation
Vision and AI Lab, Indian Institute of Science, Bangalore · University of Maryland, Baltimore County · IIT Hyderabad
Learning More from Less: Unlocking Internal Representations for Benchmark Compression
Jan 31, 2026Yueqi Zhang, Jin Hu, Shaoxiong Feng +9LLM Evaluation Representation Learning
School of Computer Science, Beijing Institute of Technology · Xiaohongshu Inc.
Are Current Continual Learning Methods Truly Agnostic? Introducing OPRE, a Step Toward Agnostic Continual Learning
Nov 11, 2025Raphaël Bayle, Martial Mermillod, Robert M. FrenchContinual Learning Coreset Selection
Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LPNC, Grenoble, France · LEAD-CNRS UMR 5022, U. de Bourgogne Europe, Dijon, France

May 14, 2026, cs.AIJ/K move · Enter open · S save

COREKG: Coreset-Guided Personalized Summarization of Knowledge Graphs

Sohel Aman Khan, Raghava Mutharaju, Supratim Shit

Department of CSE, IIIT-Delhi, India · Mehta Family School of Data Science and AI, IIT Palakkad, India

Coreset Selection Knowledge Graphs

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Knowledge Graphs (KGs) are extensively used across different domains and in several applications. Often, these KGs are very large in size. Such KGs become unwieldy for tasks such as question answering and visualization. Summarization of KGs offers a viable alternative in such cases. Furthermore, personalized KG summarization is crucial in the current data-driven world as it captures the specific requirements of users based on their query patterns. Since it only maintains relevant information, the personalized summaries of KG are small, resulting in significantly smaller storage requirements and query runtime. In this work, we adapt the coreset theory to create personalized KG summaries. For a given dataset and a user-specific query workload, we present an approach that samples a relevant subset of triples using sensitivity-based importance sampling. We ensure that the subset approximates the characteristics of the full dataset with bounded approximation error. We define sensitivity scores that measure the importance of a triple with respect to a user's query workload, which are then used by our coreset construction algorithm. We explicitly focus on personalized knowledge graph summarization by constructing summaries independently for each user based on their query behaviour. Our evaluation on Freebase, WikiData, and DBpedia shows that COREKG delivers higher query-answering accuracy and structural coverage than the state-of-the-art methods, such as GLIMPSE, PPR, iSummary, PEGASUS and APEX

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while requiring only a tiny fraction of the original graph.

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