arXiv Machine Learning June 2, 2026 By Louise Davy, Stephan Cl\'emen\c{c}on, Charlotte Laclau

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

Read the original on arXiv Machine Learning →

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.

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

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Related stories

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

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CLoVE: Personalized Federated Learning through Clustering of Loss Vector Embeddings

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