arXiv Machine Learning July 9, 2026 By Muhammadjon Tursunbadalov (School of Science and Technology, Champions College Prep, United States), Mustafojon Tursunbadalov (School of Science and Technology, Champions College Prep, United States)

A Quiet Failure in Calibrated Virtual Screening: Marginal Conformal Prediction Under-Covers the Minority Class, and a Class-Conditional Fix Recovers It

Read the original on arXiv Machine Learning →

arXiv:2607. 06605v1 Announce Type: new Abstract: Conformal prediction is being adopted in drug discovery to put an honest number on model reliability: pick an error rate alpha, and the method returns prediction sets containing the true label with probability at least 1 - alpha.

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

arXiv Machine Learning

Jul 28

Robust Conformalized Selection with Noisy Responses

arXiv:2607. 22985v1 Announce Type: cross Abstract: Conformalized selection has been widely applied to select high-quality candidates from large datasets with rigorous uncertainty quantification, such as reliable labeling, drug discovery, and the alignment of large language models.

By Chengyao Yu, Hongxin Wei, Bingyi Jing

llms safety

arXiv Machine Learning

Jul 30

Cost-Sensitive Conformal Prediction and Human-in-the-Loop Abstention for Imbalanced High-Stakes Decision Support: A Multi-Domain Benchmark

arXiv:2607. 27143v1 Announce Type: new Abstract: High-stakes decision systems in credit scoring, fraud detection, healthcare, and industrial safety require reliable uncertainty quantification under severe class imbalance and asymmetric error costs.

By Manpreet Singh, Akshatha Srikantha, Shyamal Lakhanpal

benchmarks safety

arXiv Machine Learning

Jun 25

Model-agnostic Mitigation Strategies of Data Imbalance for Regression

arXiv:2506. 01486v2 Announce Type: replace Abstract: Data imbalance persists as a pervasive challenge in regression tasks, introducing bias in model performance and undermining predictive reliability.

By Jelke Wibbeke, Sebastian Rohjans, Andreas Rauh

benchmarks safety

arXiv Machine Learning

Aug 3

Mitigating Class-Tail Undercoverage in Medical Vision-Language Models under Clinical Shift

arXiv:2607. 28696v1 Announce Type: new Abstract: Medical vision-language models (VLMs) can retain high observed marginal coverage after clinical shift while substantially under-covering an individual disease class.

By Mushir Akhtar, M. Tanveer

llms multimodal

arXiv Machine Learning

Jul 21

The Label Complexity of Class-Conditional Coverage under Distribution Shift

arXiv:2607. 18088v1 Announce Type: new Abstract: Standard evaluation of many recognition systems contains distribution shift by construction, since benchmarks place disjoint conditions in the training and test splits.

By Weijia Han, Lisha Qu

benchmarks

arXiv Machine Learning

Jul 14

Scaffold splits hide structural-frontier failures in ADMET models

arXiv:2607. 10729v1 Announce Type: new Abstract: Molecular property models are commonly evaluated by holding out Bemis--Murcko scaffolds, yet a scaffold identifier is only one notion of chemical unfamiliarity.

By Jiacheng Zheng, Chang Guo, Zixuan Wang, Xinyu Liu

Related stories

Robust Conformalized Selection with Noisy Responses

Cost-Sensitive Conformal Prediction and Human-in-the-Loop Abstention for Imbalanced High-Stakes Decision Support: A Multi-Domain Benchmark

Model-agnostic Mitigation Strategies of Data Imbalance for Regression

Mitigating Class-Tail Undercoverage in Medical Vision-Language Models under Clinical Shift

The Label Complexity of Class-Conditional Coverage under Distribution Shift

Scaffold splits hide structural-frontier failures in ADMET models