OpenNovelty.org

Conformal Prediction for Long-Tailed Classification

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
conformal predictionuncertainty quantificationlong tailclass imbalancefine-grained image classification
Abstract:

Many real-world classification problems, such as plant identification, have extremely long-tailed class distributions. In order for prediction sets to be useful in such settings, they should (i) provide good class-conditional coverage, ensuring that rare classes are not systematically omitted from the prediction sets, and (ii) be a reasonable size, allowing users to easily verify candidate labels. Unfortunately, existing conformal prediction methods, when applied to the long-tailed setting, force practitioners to make a binary choice between small sets with poor class-conditional coverage or sets with very good class-conditional coverage but that are extremely large. We propose methods with guaranteed marginal coverage that smoothly trade off between set size and class-conditional coverage. First, we introduce a new conformal score function, coined prevalence-adjusted softmax, that targets macro-coverage, a relaxed notion of class-conditional coverage. Second, we propose a label-weighted conformal prediction method that allows us to interpolate between marginal and class-conditional conformal prediction. We demonstrate our methods on Pl@ntNet-300K and iNaturalist-2018, two long-tailed image datasets with 1,081 and 8,142 classes, respectively.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes prevalence-adjusted softmax (PAS) and label-weighted conformal prediction methods to address long-tailed classification, targeting a smooth trade-off between set size and class-conditional coverage. It resides in the 'Prevalence-Adjusted and Label-Weighted Approaches' leaf under 'Class-Conditional Coverage Methods', sharing this leaf with only one sibling paper (Conformal Long-tail). This indicates a relatively sparse research direction within the broader taxonomy of 35 papers across multiple branches, suggesting the specific combination of prevalence adjustment and label weighting for long-tailed conformal prediction remains underexplored.

The taxonomy reveals that class-conditional coverage methods branch into three distinct approaches: prevalence-adjusted techniques (where this paper sits), rank calibration methods, and Mondrian cross-conformal partitioning. Neighboring branches include training-based frameworks that integrate conformal prediction into learning loops and empirical studies examining distribution shift. The paper's post-hoc calibration strategy contrasts with training-integrated methods like Set-Valued Classification Loss, while its focus on class imbalance connects to fairness-aware conformal methods in adjacent branches. The taxonomy's scope notes clarify that this work excludes rank-based calibration and Mondrian partitioning, positioning it as a distinct approach within the class-conditional coverage landscape.

Among 29 candidates examined through limited semantic search, none of the three core contributions—PAS score function (9 candidates examined), INTERP-Q interpolation procedure (10 candidates), and WPAS extension (10 candidates)—were clearly refuted by prior work. The PAS score function, which adjusts softmax outputs by class prevalence to target macro-coverage, appears novel within the examined scope. The INTERP-Q method for interpolating between marginal and class-conditional thresholds similarly lacks direct precedent among the candidates reviewed. The WPAS extension, combining both techniques, also shows no overlapping prior work in the limited search, though the small candidate pool and single sibling paper suggest the analysis captures a narrow slice of potentially relevant literature.

Based on the top-29 semantic matches and the sparse taxonomy leaf (one sibling paper), the work appears to introduce distinct technical mechanisms for balancing coverage and set size in long-tailed settings. However, the limited search scope and the presence of only one closely related paper in the taxonomy leaf mean this assessment reflects novelty within a constrained comparison set rather than exhaustive field coverage. Broader searches or examination of rank calibration and Mondrian methods might reveal additional connections not captured here.

Taxonomy

Core-task Taxonomy Papers
35
3
Claimed Contributions
29
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Conformal prediction for long-tailed classification problems. The field of conformal prediction has expanded to address diverse challenges in uncertainty quantification, with the taxonomy revealing several major branches. Class-Conditional Coverage Methods focus on achieving valid coverage guarantees for individual classes, particularly through prevalence-adjusted and label-weighted approaches that handle imbalanced data. Training-Based and Integrated Frameworks explore how conformal techniques can be embedded during model training or combined with loss functions, as seen in works like Set-Valued Classification Loss[3]. Domain-Specific Applications demonstrate the versatility of conformal methods across medical imaging, autonomous driving, and other specialized contexts, while Empirical Evaluation and Robustness Studies investigate performance under distribution shift and real-world conditions. Additional branches address Explainability and Fairness Integration, Open-Set and Survival Analysis Extensions, and Threshold Optimization, reflecting the breadth of methodological innovation and practical deployment scenarios. A particularly active line of work centers on adapting conformal prediction to class imbalance and long-tailed distributions, where standard methods may produce overly conservative prediction sets for rare classes. Conformal Long-Tailed[0] sits squarely within the Class-Conditional Coverage Methods branch, specifically under Prevalence-Adjusted and Label-Weighted Approaches, addressing the challenge of maintaining valid coverage across all classes despite severe imbalance. This work shares thematic concerns with Conformal Long-tail[1], which also tackles long-tailed scenarios, and contrasts with training-integrated approaches like Set-Valued Classification Loss[3] that modify the learning objective itself. Meanwhile, fairness-aware methods such as Fairness Skin Lesion[4] and explainability tools like ConformaSight[5] highlight complementary dimensions—ensuring equitable coverage and interpretability—that intersect with but extend beyond the core imbalance problem. The landscape reveals ongoing tension between post-hoc calibration strategies and deeper integration with model training, with Conformal Long-Tailed[0] exemplifying the former by adjusting thresholds to respect class-specific prevalence.

Claimed Contributions

Prevalence-adjusted softmax (PAS) conformal score function

The authors derive an oracle-optimal set form for trading off set size and macro-coverage, then propose the PAS score function that approximates these oracle sets by thresholding on estimated probability ratios adjusted by class prevalence. This score function is designed to achieve better macro-coverage than standard methods in long-tailed settings.

9 retrieved papers
INTERP-Q procedure for interpolating conformal thresholds

The authors introduce INTERP-Q, which constructs prediction sets by linearly interpolating between CLASSWISE and STANDARD conformal quantile thresholds using a tunable parameter. This allows practitioners to smoothly trade off between set size and class-conditional coverage while maintaining marginal coverage guarantees.

10 retrieved papers
Weighted prevalence-adjusted softmax (WPAS) extension

The authors extend PAS to WPAS, which allows users to specify class-dependent weights to prioritize coverage of certain classes (such as endangered species). This weighted score function optimizes for weighted macro-coverage rather than uniform macro-coverage across all classes.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Prevalence-adjusted softmax (PAS) conformal score function

The authors derive an oracle-optimal set form for trading off set size and macro-coverage, then propose the PAS score function that approximates these oracle sets by thresholding on estimated probability ratios adjusted by class prevalence. This score function is designed to achieve better macro-coverage than standard methods in long-tailed settings.

Contribution

INTERP-Q procedure for interpolating conformal thresholds

The authors introduce INTERP-Q, which constructs prediction sets by linearly interpolating between CLASSWISE and STANDARD conformal quantile thresholds using a tunable parameter. This allows practitioners to smoothly trade off between set size and class-conditional coverage while maintaining marginal coverage guarantees.

Contribution

Weighted prevalence-adjusted softmax (WPAS) extension

The authors extend PAS to WPAS, which allows users to specify class-dependent weights to prioritize coverage of certain classes (such as endangered species). This weighted score function optimizes for weighted macro-coverage rather than uniform macro-coverage across all classes.

Table of Contents