Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· 0 citations· 9 references
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.
Developing nonlinear models that are both expressive and computationally efficient remains a challenge in machine learning and nonlinear system identification. Tensor network kernel machines (TNKM) address this challenge by combining nonlinear feature representations with compact low-rank tensor-network parameterizatio...
This survey provides a structured entry point to tensorized language models and clarifies when parameter savings can plausibly translate into memory efficiency, computational efficiency, or interpretability, and introduces a metric for the compression-realization gap between theoretical memory reduction and measured sy...
M. Tarasov, Salman Ahmadi-Asl, A. D. de Almeida et al.· 1 citation
This paper proposes a novel Tensor Train (TT)-based tensor-on-tensor regression optimization framework for variable selection based on mode-1 hyperslice sparsity, and designs an alternating iterative algorithm equipped with a preconditioned metric to efficiently solve the proposed model.
In recent years, weak-form methods have made significant advances in data-driven discovery of dynamical systems. However, in high-dimensional settings, current techniques can prove expensive in both computation and memory. In this work, we introduce TT-WSINDy, which combines techniques of the Multidimensional Approxima...
W. Houser, Vanja Dukic, David M. Bortz· 0 citations
This work presents a method to accelerate the optimization of learning high dimensional functions using deep neural network (DNN), and studies the effect of adding features which distill pretrained DNN into TNs using a discretize and decompose strategy.
This thesis builds on an existing diagnostics toolkit mainly for t-SNE and UMAP and turns it into a more accessible package for interested practitioners, while also extending it with diagnostics tools.
Kasra Amirani, S. Huisman, E. V. van Nieuwenburg· 0 citations
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