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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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8 results for “tensor-on-tensor”

Tensor-on-Tensor Regression: Riemannian Optimization, Over-parameterization, Statistical-computational Gap, and Their Interplaymath.ST

New methods solve tensor-on-tensor regression with unknown rank, revealing benefits of over-parameterization.

problem Connecting tensor responses to tensor covariates with unknown intrinsic rank.

method Riemannian gradient descent and Riemannian Gauss-Newton methods for tensor-on-tensor regression.

result Riemannian optimization methods converge linearly and quadratically to a statistically optimal estimate in rank over-parameterized settings.

tensor-on-tensor regression Riemannian optimization over-parameterization statistical-computational gap Tucker rank

2022-06-17abs ↗pdf ↗

Efficient Decision Trees for Tensor Regressionscs.LG

A new tree method for tensor data improves regression accuracy.

problem Efficiently modeling tensor data for regression problems.

method Scalar-output regression tree models for scalar-on-tensor problems, and tensor-on-tensor problems using additive tree ensemble approaches.

result The tensor-input tree (TT) method outperforms tensor-input GP models in efficiency and accuracy.

tensor regression decision trees scalar-on-tensor tensor-on-tensor regression accuracy

2024-08-04abs ↗pdf ↗

Tensor-on-tensor Regression Neural Networks for Process Modeling with High-dimensional Datacs.LG

TRNN combines tensor geometry with neural network nonlinearity for HD data.

problem Modeling high-dimensional data with preserved tensor geometry and nonlinear interactions.

method Introduces TRNN that integrates tensor geometry and neural network nonlinearity.

result TRNN preserves tensor geometry while offering nonlinearity.

Tensor-on-Tensor Regression Neural Network High-dimensional Data Tensor Geometry Nonlinear Interactions Industrial Processes

2025-10-06abs ↗pdf ↗

Factor Augmented Tensor-on-Tensor Neural Networksstat.ML

Proposes FATTNN for tensor-on-tensor regression with improved prediction and reduced computation.

problem Tensor-on-tensor regression with complex tensor structures and nonlinear relationships.

method Integrates tensor factor models into deep neural networks to handle nonlinearity and reduce data dimensionality.

result Significant improvements in prediction accuracy and computational efficiency over traditional methods.

tensor-on-tensor regression factor augmented neural networks tensor factor models deep learning nonlinear relationships

2024-05-30abs ↗pdf ↗

Robust Tensor-on-Tensor Regressionstat.ME

Robust TOT regression method handles outliers in tensor data.

problem Outliers in tensor data affect standard TOT regression.

method ROTOT method using a single loss function for outliers and robust MPCA for predictor.

result ROTOT method reduces influence of both casewise and cellwise outliers.

Tensor data Regression Outliers Robust methods Multilinear PCA

2026-03-26abs ↗pdf ↗

Tensor-on-tensor regressionstat.ME

We propose a framework for the linear prediction of a multi-way array (i.e., a tensor) from another multi-way array of arbitrary dimension, using the contracted tensor product. This framework generalizes several existing approaches, including methods to predict a scalar outcome from a tensor, a matrix from a matrix, or…

2017-01-04abs ↗pdf ↗

Reduced-Rank Tensor-on-Tensor Regression and Tensor-variate Analysis of Variancestat.ME

Extends multivariate regression for tensor-variate data, identifying brain regions and facial characteristics.

problem Challenges in fitting regression models with multivariate responses and covariates.

method Low-rank tensor formats on regression coefficients and tensor-variate normal distribution for errors.

result Maximum likelihood estimators for tensor-on-tensor regression via block-relaxation algorithms.

tensor regression tensor-variate analysis functional MRI Kronecker separable ethnic origin

2020-12-18abs ↗pdf ↗

Elliptically-Contoured Tensor-variate Distributions with Application to Improved Image Learningstat.ME

Improved image learning using elliptically contoured tensor-variate distributions.

problem Inadequate statistical analysis for tensor-valued data, especially with heavier or lighter tails.

method Developed a family of elliptically contoured tensor-variate distributions and derived their properties and procedures for estimation.

result Tensor-variate classification rules and tensor-on-tensor regression better predict and characterize data than TVN-based methods.

tensor-variate elliptically contoured image learning statistical analysis discriminant analysis

2022-11-13abs ↗pdf ↗