Imagen de Portada

Regression-aware decompositions

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autor principal:	Tygert, Mark
Formato:	Preprint
Publicado:	2017
Materias:	Methodology Machine Learning Numerical Analysis
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Latent Autoencoder Ensemble Kalman Filter for Nonlinear Data assimilation
por: Tong, Xin T., et al.
Publicado: (2026)

Kinetic Langevin Splitting Schemes for Constrained Sampling
por: Chada, Neil K., et al.
Publicado: (2026)

Multivariate Density Estimation via Variance-Reduced Sketching
por: Peng, Yifan, et al.
Publicado: (2024)

Variational Markov chain mixtures with automatic component selection
por: Miles, Christopher E., et al.
Publicado: (2024)

Parameter Inference based on Gaussian Processes Informed by Nonlinear Partial Differential Equations
por: Li, Zhaohui, et al.
Publicado: (2022)

Calibration of P-values for calibration and for deviation of a subpopulation from the full population
por: Tygert, Mark
Publicado: (2022)

Median of Means Sampling for the Keister Function
por: Zhang, Bocheng
Publicado: (2025)

Tensor Decomposition with Unaligned Observations
por: Tang, Runshi, et al.
Publicado: (2024)

Numerically robust Gaussian state estimation with singular observation noise
por: Krämer, Nicholas, et al.
Publicado: (2025)

Zero-Truncated Poisson Regression for Sparse Multiway Count Data Corrupted by False Zeros
por: López, Oscar, et al.
Publicado: (2022)

Stein transport for Bayesian inference
por: Nüsken, Nikolas
Publicado: (2024)

Tensor Methods in High Dimensional Data Analysis: Opportunities and Challenges
por: Auddy, Arnab, et al.
Publicado: (2024)

Revisit CP Tensor Decomposition: Statistical Optimality and Fast Convergence
por: Tang, Runshi, et al.
Publicado: (2025)

Second Order Ensemble Langevin Method for Sampling and Inverse Problems
por: Liu, Ziming, et al.
Publicado: (2022)

Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics
por: Paulin, Daniel, et al.
Publicado: (2024)

Non-Asymptotic Analysis of Ensemble Kalman Updates: Effective Dimension and Localization
por: Ghattas, Omar Al, et al.
Publicado: (2022)

Mode-wise Principal Subspace Pursuit and Matrix Spiked Covariance Model
por: Tang, Runshi, et al.
Publicado: (2023)

The Ensemble Epanechnikov Mixture Filter
por: Popov, Andrey A., et al.
Publicado: (2024)

On computing and the complexity of computing higher-order $U$-statistics, exactly
por: Chen, Xingyu, et al.
Publicado: (2025)

Deep Neural-network Prior for Orbit Recovery from Method of Moments
por: Khoo, Yuehaw, et al.
Publicado: (2023)

Randomized algorithms for distributed computation of principal component analysis and singular value decomposition
por: Li, Huamin, et al.
Publicado: (2016)

Deep Learning for Subspace Regression
por: Fanaskov, Vladimir, et al.
Publicado: (2025)

Secure multiparty computations in floating-point arithmetic
por: Guo, Chuan, et al.
Publicado: (2020)

Neural network-driven domain decomposition for efficient solutions to the Helmholtz equation
por: Dolean, Victorita, et al.
Publicado: (2025)

Fast and interpretable Support Vector Classification based on the truncated ANOVA decomposition
por: Akhalaya, Kseniya, et al.
Publicado: (2024)

Domain decomposition architectures and Gauss-Newton training for physics-informed neural networks
por: Heinlein, Alexander, et al.
Publicado: (2025)

Geometry-aware training of factorized layers in tensor Tucker format
por: Zangrando, Emanuele, et al.
Publicado: (2023)

A decomposition-based robust training of physics-informed neural networks for nearly incompressible linear elasticity
por: Dick, Josef, et al.
Publicado: (2025)

A Correlation-induced Finite Difference Estimator
por: Liang, Guo, et al.
Publicado: (2024)

Understanding In-Context Learning for Nonlinear Regression with Transformers: Attention as Featurizer
por: Hsu, Alexander, et al.
Publicado: (2026)

Neural Parameter Regression for Explicit Representations of PDE Solution Operators
por: Mundinger, Konrad, et al.
Publicado: (2024)

Symmetry group based domain decomposition to enhance physics-informed neural networks for solving partial differential equations
por: Liu, Ye, et al.
Publicado: (2024)

Mode-Shape Expansion Using Physics-Constrained Gaussian Process Regression
por: Ghahari, Farid
Publicado: (2026)

Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers
por: Pförtner, Marvin, et al.
Publicado: (2022)

Solving All Regression Models For Learning Gaussian Networks Using Givens Rotations
por: Alipourfard, Borzou, et al.
Publicado: (2019)

A model-data asymptotic-preserving neural network method based on micro-macro decomposition for gray radiative transfer equations
por: Li, Hongyan, et al.
Publicado: (2022)

Efficient and Precise Calculation of the Confluent Hypergeometric Function
por: Herschtal, Alan
Publicado: (2024)

ARMA approximation of a Non-separable Spatio-Temporal Model with Fractional Smoothnesses in Space and Time
por: Furset, S. Knutsen, et al.
Publicado: (2026)

Beyond Independence: on Jointly Normal Priors in Bayesian Inversion
por: Nicholson, Ruanui, et al.
Publicado: (2026)

Ridge detection for nonstationary multicomponent signals with time-varying wave-shape functions and its applications
por: Su, Yan-Wei, et al.
Publicado: (2023)