arXiv Statistics ML
Adaptive Estimation and Inference in Conditional Moment Models via the Discrepancy Principle
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Adversarial Contamination Meets Hard Thresholding: An Iterative Algorithm with Signal Adaptivity and Minimax Optimality
arXiv:2606. 27685v1 Announce Type: cross Abstract: Pervasive data contamination -- stemming from measurement errors, outliers, or adversarial corruption -- has motivated the development of robust statistical methods.
arXiv AI
Sep 10
Deep Barycentric Regression for Optimal Transport Map Estimation and its Statistical Optimality
The paper introduces BROT, a two‑step approach for estimating optimal transport maps. First, it computes the unregularized OT plan, then fits a deep neural network to the resulting barycentric targets using least‑squares regression. The authors prove that, under standard regularity conditions, BROT achieves the minimax convergence rate when the true OT map is Lipschitz, and demonstrate its effectiveness on synthetic data, images, and downstream tasks such as single‑cell perturbation prediction and unsupervised domain adaptation.
arXiv Statistics ML
Oct 1
Distributionally robust linear regression through the lens of adversarial training
arXiv:2609. 39449v1 Announce Type: new Abstract: Distributionally robust optimization (DRO) studies parameter estimation under uncertainty in the underlying probability distribution and has emerged as a principled framework for analyzing robustness and generalization.
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.
arXiv Machine Learning
Aug 14
Statistical Properties of Robust Learning under Distributional Shifts
arXiv:2608. 13133v1 Announce Type: cross Abstract: Distributional shifts arise when the target deployment environment differs from the source environment that generated the training data.
arXiv Machine Learning
Aug 14
The Noise Premium in Adversarial Training for Kernel Regression
arXiv:2607. 27995v2 Announce Type: replace-cross Abstract: Adversarial training can improve the robustness of predictive models to bounded perturbations, often at the cost of statistical efficiency.