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Interplay Between Classical Tensor Methods And Foundation Models

Dawon Ahn Yong-chan Park Taehyung Kwon Jun-Gi Jang Kijung Shin U. Kang E. Papalexakis
Aug 2026 · Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 · pp. 13403-13404 · 0 citations · 5 references

TL;DR

This workshop aims to provide a forum for advancing tensor methods and applying them to various applications at the intersection of data mining and modern machine learning to foster an interactive environment for researchers to exchange ideas and build connections across communities.

Abstract

Tensor methods have played a central role in analyzing multi-dimensional data across a wide range of real-world applications. At the same time, these methods provide low-rank representations, which enable trustworthy and parsimonious machine learning systems. However, even though they hold great promise, tensor methods remain relatively underexplored in the context of modern neural architectures and foundation models. This workshop aims to provide a forum for advancing tensor methods and applying them to various applications at the intersection of data mining and modern machine learning. Supported by organizers and keynote speakers with broad expertise across machine learning, signal processing, and data mining, the workshop aims to foster an interactive environment for researchers to exchange ideas and build connections across communities.

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