Subgroup Robustness

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10 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Active Learning for Finding a Needle in a Haystack
Oct 10, 2026Emily Silcock, Simon LöweSubgroup Robustness Active Learning
Harvard University · Burning Glass Institute
Stochastic Grouping Conformal Prediction for Effective Subgroup Reliability
Oct 8, 2026Meihui Zhong, Wenxin Tai, Ting Zhong +1Fair Conformal Prediction Conformal Prediction
University of Electronic Science and Technology of China · Kashi Institute of Electronics and Information Industry · Intelligent Digital Media Technology, Key Laboratory of Sichuan Province
Distributionally Robust Survival Models under Subpopulation Shift and Outlier Contamination
Oct 2, 2026Seonghwi Kim, Sung Ho Jo, Minwoo ChaeSubgroup Robustness Distribution Shift Robustness
Pohang University of Science and Technology
AIA $^{2}$ : Attribute-Agnostic Imbalance Augmentation for Subgroup Robustness
Aug 31, 2026Hanshu Rao, Guangzeng Han, Xiaolei HuangClass-Imbalanced Learning Subgroup Robustness
University of Memphis · University of Missouri
ProME: Prototype-Margin Environments with Repair-Aware Selection for Group-Robust Learning
Aug 13, 2026Qianqian Wang, Yunshan Li, Dawei Huang +2Distribution Shift Robustness Spurious Correlation Robustness
aShenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet, Shenzhen, China · bDepartment of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, China
Beyond Metadata: CAPRA for Hidden Subgroup Analysis under Missing Metadata in Medical Imaging
Jul 10, 2026Yawen Li, Yan Li, Zhe Xue +3Subgroup Robustness Medical Image Analysis
Beijing University of Posts and Telecommunications Beijing, China
Discovering Latent Groups for Robust Classification
Jun 22, 2026Ankur Garg, Ulrich Aïvodji, Samira Ebrahimi Kahou +1Subgroup Robustness Interpretable ML
DuetFair: Coupling Inter- and Intra-Subgroup Robustness for Fair Medical Image Segmentation
May 11, 2026Yiqi Tian, Sangjoon Park, Bo Zeng +3Subgroup Robustness Healthcare
Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114 · Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, PA 15261 · Department of Radiation Oncology, College of Medicine, Yonsei University, Seoul, South Korea +2
Rank-Constrained Adaptation for Reliable Real-World Performance
Feb 6, 2026Abinitha Gourabathina, Hyewon Jeong, Teya Bergamaschi +2Subgroup Robustness Distribution Shift Robustness
Department of Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA · Harvard School of Medicine, Harvard, Boston, MA, USA.
When does Subgroup Balance Matter? Predicting Performance Sensitivity from Latent Separability
Dec 10, 2025Anissa Alloula, Charles Jones, Zuzanna Wakefield-Skorniewska +2Subgroup Robustness Dataset Bias
University of Oxford · Imperial College London

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Active Learning for Finding a Needle in a Haystack

Emily Silcock, Simon Löwe

Harvard University · Burning Glass Institute

Subgroup Robustness Active Learning Class-Imbalanced Learning

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Training a classifier to find a rare class of documents, such as hate speech directed at a particular religious group, requires labelled examples that are expensive to obtain when the class makes up only a small fraction of a corpus. Active learning reduces this cost, but it must start from an initial labelled set that contains examples of the target class. Random sampling cannot supply these at reasonable cost, so they must be selected in some other way, such as keyword search, and any such selection biases the initial set towards part of the target class. We evaluate whether active learning can correct this bias. On AG News and Measuring Hate Speech, at a prevalence of around 1%, we compare random sampling with eight active learning strategies, starting from initial sets that cover or omit known subgroups of the target class, and measure recall on the omitted subgroups as well as F1. On AG News, almost every strategy recovers the omitted subgroup. On Measuring Hate Speech, uncertainty-based strategies recover less of the omitted subgroups than random sampling, whereas discriminative active learning recovers 55% of them without a significant loss of F1. Aggregate metrics conceal these differences.