Cover Image

A probabilistic view on Riemannian machine learning models for SPD matrices

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	de Surrel, Thibault, Yger, Florian, Lotte, Fabien, Chevallier, Sylvain
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Statistics Theory
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Wrapped Gaussian on the manifold of Symmetric Positive Definite Matrices
by: de Surrel, Thibault, et al.
Published: (2025)

A review of predictive uncertainty estimation with machine learning
by: Tyralis, Hristos, et al.
Published: (2022)

META-ANOVA: Screening interactions for interpretable machine learning
by: Choi, Yongchan, et al.
Published: (2024)

Statistical Agnostic Regression: a machine learning method to validate regression models
by: Gorriz, Juan M, et al.
Published: (2024)

Adaptive debiased machine learning using data-driven model selection techniques
by: van der Laan, Lars, et al.
Published: (2023)

Structure-Preserving Transformers for Sequences of SPD Matrices
by: Seraphim, Mathieu, et al.
Published: (2023)

On the physics of nested Markov models: a generalized probabilistic theory perspective
by: Zhang, Xingjian, et al.
Published: (2024)

An analysis of the noise schedule for score-based generative models
by: Strasman, Stanislas, et al.
Published: (2024)

Riemannian Proximal Sampler for High-accuracy Sampling on Manifolds
by: Guan, Yunrui, et al.
Published: (2025)

A spectral method for multi-view subspace learning using the product of projections
by: Sergazinov, Renat, et al.
Published: (2024)

Riemannian Principal Component Analysis
by: Rodríguez, Oldemar
Published: (2025)

Semiparametric inference for impulse response functions using double/debiased machine learning
by: Ballinari, Daniele, et al.
Published: (2024)

Categorical and geometric methods in statistical, manifold, and machine learning
by: Lê, Hông Vân, et al.
Published: (2025)

Iteratively reweighted kernel machines efficiently learn sparse functions
by: Zhu, Libin, et al.
Published: (2025)

Learning manifold diffusion semigroups from graph transition matrices
by: Cheng, Xiuyuan, et al.
Published: (2026)

Adaptive deep learning for nonlinear time series models
by: Kurisu, Daisuke, et al.
Published: (2022)

A comparative study of conformal prediction methods for valid uncertainty quantification in machine learning
by: Dewolf, Nicolas
Published: (2024)

Estimating shared subspace with AJIVE: the power and limitation of multiple data matrices
by: Yang, Yuepeng, et al.
Published: (2025)

Denoising diffusion probabilistic models are optimally adaptive to unknown low dimensionality
by: Huang, Zhihan, et al.
Published: (2024)

Generalizing while preserving monotonicity in comparison-based preference learning models
by: Fageot, Julien, et al.
Published: (2025)

Marginal and training-conditional guarantees in one-shot federated conformal prediction
by: Humbert, Pierre, et al.
Published: (2024)

Total Variation Rates for Riemannian Flow Matching
by: Guan, Yunrui, et al.
Published: (2026)

A general framework for deep learning
by: Kengne, William, et al.
Published: (2025)

Efficient Sampling on Riemannian Manifolds via Langevin MCMC
by: Cheng, Xiang, et al.
Published: (2024)

Nonlinear spiked covariance matrices and signal propagation in deep neural networks
by: Wang, Zhichao, et al.
Published: (2024)

Deep Horseshoe Gaussian Processes
by: Castillo, Ismaël, et al.
Published: (2024)

Precise analysis of ridge interpolators under heavy correlations -- a Random Duality Theory view
by: Stojnic, Mihailo
Published: (2024)

Wasserstein Convergence of Critically Damped Langevin Diffusions
by: Strasman, Stanislas, et al.
Published: (2025)

Sharp asymptotic theory for Q-learning with LDTZ learning rate and its generalization
by: Bonnerjee, Soham, et al.
Published: (2026)

Surveying the space of descriptions of a composite system with machine learning
by: Murphy, Kieran A., et al.
Published: (2024)

Statistically guided deep learning
by: Kohler, Michael, et al.
Published: (2025)

Physics-informed kernel learning
by: Doumèche, Nathan, et al.
Published: (2024)

Semi-Modular Inference: enhanced learning in multi-modular models by tempering the influence of components
by: Carmona, Chris U., et al.
Published: (2020)

On the consistency of supervised learning with missing values
by: Josse, Julie, et al.
Published: (2019)

Nonparametric logistic regression with deep learning
by: Yara, Atsutomo, et al.
Published: (2024)

Optimal structure learning and conditional independence testing
by: Gao, Ming, et al.
Published: (2025)

On the rates of convergence for learning with convolutional neural networks
by: Yang, Yunfei, et al.
Published: (2024)

Statistical learning theory and Occam's razor: The core argument
by: Sterkenburg, Tom F.
Published: (2023)

Robust deep learning from weakly dependent data
by: Kengne, William, et al.
Published: (2024)

Strong identifiability and parameter learning in regression with heterogeneous response
by: Do, Dat, et al.
Published: (2022)