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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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10 results for “tensor-variate”

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 ↗

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 ↗

tvGP-VAE: Tensor-variate Gaussian Process Prior Variational Autoencodercs.LG

tvGP-VAE models tensor-valued latent variables with Gaussian processes for better data structure representation.

problem Agnostic latent variables in VAEs ignore data structure correlations.

method Proposes tensor-variate Gaussian process prior for variational autoencoder.

result Explicitly modeling correlation structures improves model performance in reconstruction.

VAE Gaussian process tensor-variate latent variables correlation modeling

2020-06-08abs ↗pdf ↗

Mixtures of spatial factor analyzers for tensor-variate datastat.ME

MSFA clusters high-dimensional spatial data using spline-based covariance structures.

problem Clustering high-dimensional spatial data with flexible covariance structures.

method Mixture of spatial factor analyzers with spline-based covariance and matrix variate factor analyzers for dimensionality reduction.

result Proposed models accurately infer and differentiate distinct spatial patterns in tensor-variate data.

spatial factor analyzers tensor-variate data spline-based covariance dimensionality reduction Raman spectroscopy

2026-07-08abs ↗pdf ↗

GmGM: a Fast Multi-Axis Gaussian Graphical Modelstat.ML

GmGM models multi-axis data for faster analysis.

problem Efficiently modeling multi-axis data across multiple tensors.

method Generalizes Gaussian Graphical Model to learn sparse graph representations across shared axes.

result Achieves significant speedup (order of magnitude) for large multi-modal datasets.

Gaussian Graphical Model multi-omics tensor-variate data eigendecomposition multi-axis

2022-11-05abs ↗pdf ↗

A Minimax Lower Bound for Low-Rank Matrix-Variate Logistic Regressioncs.LG

Paper finds a lower bound for estimating low-rank matrices in logistic regression.

problem Estimating low-rank coefficient matrices in logistic regression.

method Derives a minimax lower bound on the risk.

result The bound depends on matrix dimensions, rank, and sample size.

logistic regression minimax lower bound low-rank matrix sample complexity tensor-variate

2021-05-31abs ↗pdf ↗

Tensor-Dictionary Learning with Deep Kruskal-Factor Analysisstat.ML

A multi-way factor analysis model is introduced for tensor-variate data of any order. Each data item is represented as a (sparse) sum of Kruskal decompositions, a Kruskal-factor analysis (KFA). KFA is nonparametric and can infer both the tensor-rank of each dictionary atom and the number of dictionary atoms. The model …

2016-12-08abs ↗pdf ↗

SG-PALM: a Fast Physically Interpretable Tensor Graphical Modelstat.ML

SG-PALM learns interpretable tensor models for high-dimensional data.

problem Learning interpretable tensor models for high-dimensional data.

method SG-PALM combines Sylvester generative model and fast proximal alternating linearized minimization.

result SG-PALM converges linearly to global optimum and scales to high dimensions.

tensor graphical models scalable inference physical interpretability sylvester model salamander

2021-05-26abs ↗pdf ↗

Scalable Multi-Task Gaussian Process Tensor Regression for Normative Modeling of Structured Variation in Neuroimaging Datastat.ML

Most brain disorders are very heterogeneous in terms of their underlying biology and developing analysis methods to model such heterogeneity is a major challenge. A promising approach is to use probabilistic regression methods to estimate normative models of brain function using (f)MRI data then use these to map variat…

2018-07-31abs ↗pdf ↗

TEAFormers: TEnsor-Augmented Transformers for Multi-Dimensional Time Series Forecastingcs.LG

TEAFormers preserve multi-dimensional time series structures for better forecasting.

problem Traditional Transformers flatten multi-dimensional time series data, losing critical multi-dimensional relationships.

method Tensor-Augmented Transformer (TEAFormer) with Tensor-Augmentation (TEA) module.

result Significant performance enhancements in time series forecasting across benchmarks.

time series forecasting multi-dimensional data Tensor-Augmented Transformer Tensor-Augmentation attention mechanism

2024-10-27abs ↗pdf ↗