arXiv Machine Learning July 7, 2026 By Dongyue Wu, Zilin Guo, Xiaoyu Li, Jiajia Liu, Jingdong Chen, Nong Sang, Changxin Gao

Selecting Samples on Graphs: A Unified Dataset Pruning Framework for Lossless Training Acceleration

Read the original on arXiv Machine Learning →

arXiv:2606. 12913v2 Announce Type: replace Abstract: The rapid growth of modern training datasets has significantly increased computational cost, motivating dataset pruning~(DP) methods which retain only a subset of informative samples to reduce training cost.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

efficiency

arXiv Machine Learning

Jun 9

OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework

arXiv:2606. 08574v1 Announce Type: new Abstract: Data pruning (DP), as an oft-stated strategy to alleviate heavy training burdens, reduces the volume of training samples according to a well-defined pruning method while striving for near-lossless performance.

By Chenhan Jin, Shengze Xu, Qingsong Wang, Fan Jia, Dingshuo Chen, Tieyong Zeng

efficiency safety

arXiv AI

Jun 26

Learning to Select Maximum Clique Algorithms: From Traditional Machine Learning to a Dual-Channel Hybrid Neural Architecture

arXiv:2508. 08005v4 Announce Type: replace-cross Abstract: The Maximum Clique Problem (MCP) is an NP-hard problem with wide-ranging applications in fields such as bioinformatics, network science, and social computing, yet no single algorithm consistently outperforms all others across diverse graph instances.

By Xiang Li, Shanshan Wang, Chenglong Xiao

agents benchmarks

arXiv Machine Learning

Jul 30

On the Rademacher Complexity of Graph Neural Networks: Unifying Expressivity and Geometry

arXiv:2510. 10101v4 Announce Type: replace Abstract: Understanding the interplay between generalization, expressivity, and the geometry of the input space is a central challenge in graph learning.

By Martin Carrasco, Caio F. Deberaldini Netto, Vahan A. Martirosyan, Ehimare Okoyomon, Caterina Graziani

arXiv Machine Learning

Jun 2

Doing well with less! On Sampling Techniques for Empirical Pairwise Loss Estimation/Minimization

arXiv:2606. 02345v1 Announce Type: cross Abstract: Many machine learning problems, including similarity learning, ranking, and clustering, rely on empirical pairwise loss functions whose quadratic computational cost quickly becomes prohibitive at scale.

By Louise Davy, Stephan Cl\'emen\c{c}on, Charlotte Laclau

rag

arXiv Machine Learning

Jul 7

Graph Classification via Network Usable Information: From Representation Evaluation to Structure Selection

arXiv:2607. 03587v1 Announce Type: new Abstract: We propose NetinfoGC, a framework for graph classification that extends the Network Usable Information (NUI) paradigm to graph-level learning.

By Abdullah Shaik, Anwar Said

rag benchmarks

arXiv Machine Learning

Aug 11

Domain-Aware Pruning: Sparsity and Domain Generalization via Regularized Probabilistic Masking

arXiv:2608. 08624v1 Announce Type: new Abstract: Domain generalization (DG) and neural network pruning are conventionally treated as distinct objectives, targeting out-of-distribution (OOD) robustness and model efficiency, respectively.

By Parham Sazdar, Mostafa Tavassolipour, Reshad Hosseini

fine-tuning efficiency benchmarks safety

Related stories

OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework

Learning to Select Maximum Clique Algorithms: From Traditional Machine Learning to a Dual-Channel Hybrid Neural Architecture

On the Rademacher Complexity of Graph Neural Networks: Unifying Expressivity and Geometry

Doing well with less! On Sampling Techniques for Empirical Pairwise Loss Estimation/Minimization

Graph Classification via Network Usable Information: From Representation Evaluation to Structure Selection

Domain-Aware Pruning: Sparsity and Domain Generalization via Regularized Probabilistic Masking