arXiv Machine Learning August 4, 2026 By Ying Jin, Ying Jin, Dominik Rothenh\"ausler

Augmented Inverse Hybrid Weighting: Robust Inference under Deterministic and Random Distribution Shifts

Read the original on arXiv Machine Learning →

arXiv:2608. 00701v1 Announce Type: cross Abstract: Reweighting source samples to match a target covariate distribution is a standard response to distribution shift when generalizing evidence from one population to another.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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arXiv Machine Learning

Jun 11

Point-Identification of a Robust Predictor Under Latent Shift with Imperfect Proxies

arXiv:2603. 15158v2 Announce Type: replace Abstract: Addressing the domain adaptation problem becomes more challenging when distribution shifts across domains stem from latent confounders that affect both covariates and outcomes.

By Zahra Rahiminasab, Reza Soumi, Arto Klami, Samuel Kaski

fine-tuning

arXiv Machine Learning

Jul 21

Representation-Aware Distributionally Robust Optimization: A Knowledge Transfer Framework

arXiv:2509. 09371v2 Announce Type: replace-cross Abstract: Distributionally robust optimization (DRO) protects statistical learning against distributional shifts by optimizing the worst-case performance over a set of perturbed distributions.

By Zitao Wang, Nian Si, Molei Liu

fine-tuning

arXiv Machine Learning

Jun 24

Target-Aware Linear Regression Under Distribution Shift

arXiv:2606. 22775v2 Announce Type: replace-cross Abstract: Distribution shift between training and deployment is a pervasive challenge for modern AI systems.

By Zhewen Hou, Tian Zheng

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arXiv Machine Learning

5d ago

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.

By Zhiyi Li, Xiaojie Mao, Yunbei Xu, Ruohan Zhan

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arXiv AI

Jun 4

ADAPTOOD: Uncertainty-Aware Fine-Tuning for Out-of-Distribution ECG Time Series Models

arXiv:2606. 04164v1 Announce Type: cross Abstract: Data samples used for training often differ from those encountered during fine-tuning and deployment, and while ML models show promise, their performance remains limited when only small annotated datasets are available.

By Sotirios Vavaroutas, Yu Yvonne Wu, Ali Etemad, Cecilia Mascolo

fine-tuning

arXiv Machine Learning

Jun 30

Generalization error of min-norm interpolators in transfer learning

arXiv:2406. 13944v2 Announce Type: replace-cross Abstract: This paper establishes the generalization error of pooled min-$\ell_2$-norm interpolation in transfer learning, where data from diverse distributions are available.

By Yanke Song, Kenneth Gu, Sohom Bhattacharya, Pragya Sur

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