Cover Image

Aspects of a Generalized Theory of Sparsity based Inference in Linear Inverse Problems

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	O'Dowd, Ryan, Raj, Raghu G., Mhaskar, Hrushikesh N.
Format:	Preprint
Published:	2025
Subjects:	Statistics Theory
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Active Learning Classification from a Signal Separation Perspective
by: Mhaskar, Hrushikesh, et al.
Published: (2025)

A signal separation view of classification
by: Mhaskar, H. N., et al.
Published: (2025)

Learning on manifolds without manifold learning
by: Mhaskar, H. N., et al.
Published: (2024)

Efficient Bayesian Inference in Strictly Semi-parametric Linear Inverse Problems
by: Magra, Adel, et al.
Published: (2026)

Learning Without Training
by: O'Dowd, Ryan
Published: (2026)

Approximation by non-symmetric networks for cross-domain learning
by: Mhaskar, Hrushikesh
Published: (2023)

Navigating Sparsities in High-Dimensional Linear Contextual Bandits
by: Zhao, Rui, et al.
Published: (2025)

Latent-IMH: Efficient Bayesian Inference for Inverse Problems with Approximate Operators
by: Chen, Youguang, et al.
Published: (2026)

Repro Samples Method for a Performance Guaranteed Inference in General and Irregular Inference Problems
by: Xie, Minge, et al.
Published: (2024)

Moment-Based Adjustments of Statistical Inference in High-Dimensional Generalized Linear Models
by: Sawaya, Kazuma, et al.
Published: (2023)

Functional Adaptive Double-Sparsity Estimator for Functional Linear Regression Model with Multiple Functional Covariates
by: Cao, Cheng, et al.
Published: (2023)

Sparse Linear Regression is Easy on Random Supports
by: Chandrasekaran, Gautam, et al.
Published: (2025)

Generalised Bayes Linear Inference
by: Astfalck, Lachlan, et al.
Published: (2024)

Semi-parametric Bernstein-von Mises in Linear Inverse Problems
by: Magra, Adel, et al.
Published: (2023)

Statistical Inverse Problems in Hilbert Scales
by: Rastogi, Abhishake
Published: (2022)

Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems
by: Altekrüger, Fabian, et al.
Published: (2023)

Bayesian Nonparametric Inference for "Species-sampling" Problems
by: Balocchi, Cecilia, et al.
Published: (2022)

Statistical Inference for Linear Functions of Eigenvectors with Small Eigengaps
by: Agterberg, Joshua
Published: (2023)

Inference for Linear Systems with Unknown Coefficients
by: Bai, Yuehao, et al.
Published: (2026)

Simultaneous Frequentist Calibration of Confidence Regions for Multiple Functionals in Constrained Inverse Problems
by: Batlle, Pau, et al.
Published: (2025)

Consistency of Variational Inference for Nonlinear Inverse Problems of Partial Differential Equations
by: Zu, Shaokang, et al.
Published: (2024)

On Sparsity and Sub-Gaussianity in the Johnson-Lindenstrauss Lemma
by: Garivier, Aurélien, et al.
Published: (2024)

Bivariate Matrix-valued Linear Regression (BMLR): Finite-sample performance under Identifiability and Sparsity Assumptions
by: Bettache, Nayel
Published: (2024)

Inhomogeneous Priors for Bayesian Inverse Problems
by: Afkham, Babak Maboudi, et al.
Published: (2026)

Asymptotic Theory for Linear Functionals of Kernel Ridge Regression
by: Tuo, Rui, et al.
Published: (2024)

Measuring Tail Dependence in Linear Processes: Theory and Empirics
by: Datta, Debanjana, et al.
Published: (2026)

Nonparametric Instrumental Variable Analysis Without Structural Equations: Debiased Inference on Functionals of Inverse Problems with No Solutions
by: Shen, Zikai, et al.
Published: (2026)

Identifiability in Unlinked Linear Regression: Some Results and Open Problems
by: Balabdaoui, Fadoua, et al.
Published: (2025)

Unifying Different Theories of Conformal Prediction
by: Barber, Rina Foygel, et al.
Published: (2025)

G-HIVE: Parameter Estimation and Approximate Inference for Multivariate Response Generalized Linear Models with Hidden Variables
by: Lee, Inbeom, et al.
Published: (2025)

Marginal Inference for Hierarchical Generalized Linear Mixed Models with Patterned Covariance Matrices Using the Laplace Approximation
by: Hoef, Jay M. Ver, et al.
Published: (2023)

Cardinality Sparsity: Applications in Matrix-Matrix Multiplications and Machine Learning
by: Mohades, Ali, et al.
Published: (2023)

Elliptic Bayesian Inverse Problems on Metric Graphs
by: Bolin, David, et al.
Published: (2025)

Inference for Median and a Generalization of HulC
by: Paul, Manit, et al.
Published: (2024)

GMM with Many Weak Moment Conditions and Nuisance Parameters: General Theory and Applications to Causal Inference
by: Wang, Rui, et al.
Published: (2025)

Efficient Inference for Inverse Reinforcement Learning and Dynamic Discrete Choice Models
by: van der Laan, Lars, et al.
Published: (2025)

Sparsity meets correlation in Gaussian sequence model
by: Kotekal, Subhodh, et al.
Published: (2023)

Toward Optimal Statistical Inference in Noisy Linear Quadratic Reinforcement Learning over a Finite Horizon
by: Pan, Bo, et al.
Published: (2025)

Bridging Theory and Practice: Statistical Inference for Latent Space Models of Networks
by: Tian, Yuang, et al.
Published: (2026)

Quasi-maximum Likelihood Inference for Linear Double Autoregressive Models
by: Liu, Hua, et al.
Published: (2020)