arXiv Machine Learning July 10, 2026 By Abhishake Rastogi, Tapio Helin, Nicole M\"ucke

Statistical inverse learning problems with random observations

Read the original on arXiv Machine Learning →

arXiv:2312. 15341v1 Announce Type: cross Abstract: We provide an overview of recent progress in statistical inverse problems with random experimental design, covering both linear and nonlinear inverse problems.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jul 9

Statistical inverse learning and $\ell^1$-regularization

arXiv:2607. 07468v1 Announce Type: cross Abstract: We study the recovery of sparse functions from finite, noisy, and indirect observations in the framework of statistical inverse learning.

By Abhishake Rastogi, Tatiana A. Bubba, Tapio Helin, Luca Ratti

efficiency

arXiv Statistics ML

6d ago

Learning Operators by Regularized Stochastic Gradient Descent with Operator-valued Kernels

arXiv:2504.18184v5 Announce Type: replace Abstract: We consider a class of statistical inverse problems involving the estimation of a regression operator from a Polish space to a separable Hilbert sp...

By Jia-Qi Yang, Lei Shi

arXiv Machine Learning

Jun 30

Learning from samples: inverse problems over measures

arXiv:2505. 07124v3 Announce Type: replace Abstract: We study inverse problems where an unknown potential is observed only through samples from the measure it induces by a convex variational principle.

By Francisco Andrade, Gabriel Peyr\'e, Clarice Poon

arXiv Machine Learning

Jun 30

A Distributionally Robust Framework for Learned Reconstructions in Inverse Problems

arXiv:2606. 30230v1 Announce Type: cross Abstract: Learned reconstruction operators for inverse problems are typically trained under a fixed noise model, and generalize poorly when the distribution during testing differs from the one assumed during training.

By Floor van Maarschalkerwaart, Subhadip Mukherjee, Christoph Brune, Marcello Carioni

safety

arXiv Machine Learning

Sep 15

Stochastic Gradient Descent for Operator Learning in Hilbert Spaces: Convergence Rates and Minimax Lower Bounds

arXiv:2402.04691v5 Announce Type: replace-cross Abstract: This study investigates the use of stochastic gradient descent (SGD) to learn operators between general Hilbert spaces. We study weak and str...

By Lei Shi, Jia-Qi Yang

arXiv Machine Learning

Aug 31

On the Computational and Statistical Efficiency of the Empirical Maximum Entropy on the Mean Method

arXiv:2608.27705v1 Announce Type: cross Abstract: The Maximum Entropy on the Mean (MEM) method provides a flexible computational framework for solving inverse problems by combining data fidelity with...

By Matthew King-Roskamp, Gabriel Rioux, Rustum Choksi, Tim Hoheisel

One email a morning, machine-written

Related stories

Statistical inverse learning and $\ell^1$-regularization

Learning Operators by Regularized Stochastic Gradient Descent with Operator-valued Kernels

Learning from samples: inverse problems over measures

A Distributionally Robust Framework for Learned Reconstructions in Inverse Problems

Stochastic Gradient Descent for Operator Learning in Hilbert Spaces: Convergence Rates and Minimax Lower Bounds

On the Computational and Statistical Efficiency of the Empirical Maximum Entropy on the Mean Method