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Neyman-Pearson Classification under Both Null and Alternative Distributions Shift

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Imbalanced classificationTransfer LearningNeyman-Pearson Classification.
Abstract:

We consider the problem of transfer learning in Neyman–Pearson classification, where the objective is to minimize the error w.r.t. a distribution μ1\mu_1, subject to the constraint that the error w.r.t. a distribution μ0\mu_0 remains below a prescribed threshold. While transfer learning has been extensively studied in traditional classification, transfer learning in imbalanced classification such as Neyman–Pearson classification has received much less attention. This setting poses unique challenges, as both types of errors must be simultaneously controlled. Existing works address only the case of distribution shift in μ1\mu_1, whereas in many practical scenarios shifts may occur in both μ0\mu_0 and μ1\mu_1. We derive an adaptive procedure that not only guarantees improved Type-I and Type-II errors when the source is informative, but also automatically adapt to situations where the source is uninformative, thereby avoiding negative transfer. In addition to such statistical guarantees, the procedures is efficient, as shown via complementary computational guarantees.

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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.
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Overview

Overall Novelty Assessment

The paper addresses transfer learning in Neyman-Pearson classification under simultaneous shifts in both null and alternative distributions. According to the taxonomy, it occupies the 'Adaptive Transfer with Dual Shift Guarantees' leaf, which is the sole paper in this specific category. This leaf sits within the broader 'Dual Distribution Shift in Neyman-Pearson Classification' branch, indicating a relatively sparse research direction. The taxonomy contains only seven total papers across all branches, suggesting this is an emerging rather than crowded area.

The taxonomy reveals neighboring work in related but distinct directions. The 'Alternative Distribution Shift in Outlier Detection' branch contains two papers addressing shifts primarily in abnormal distributions with rare target data. The 'Robust Neyman-Pearson Criteria under Covariate Shift' branch includes two papers focusing on feature distribution changes while maintaining Neyman-Pearson principles. The taxonomy's scope notes explicitly distinguish dual-shift methods from single-distribution approaches, positioning this work at the intersection of multiple shift types where existing methods address only partial aspects of the problem.

Among twenty-five candidates examined, the first contribution (adaptive procedure for dual shifts) shows one refutable candidate from five examined, suggesting some prior work exists but coverage is limited. The second contribution (statistical guarantees under general shifts) examined ten candidates with none clearly refuting it, indicating potential novelty in the theoretical framework. The third contribution (computational guarantees via convex reduction) similarly examined ten candidates without clear refutation. The limited search scope means these statistics reflect top semantic matches rather than exhaustive coverage of the field.

Based on the limited literature search, the work appears to occupy a relatively unexplored position addressing dual distribution shifts with adaptive guarantees. The taxonomy structure and contribution-level statistics suggest novelty in simultaneously handling both distribution types while avoiding negative transfer, though the small candidate pool examined means definitive claims about field-wide novelty require broader investigation.

Taxonomy

Core-task Taxonomy Papers
7
3
Claimed Contributions
25
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: transfer learning in Neyman-Pearson classification under distribution shift. The field addresses how to maintain strict type-I error control while optimizing type-II error when both training and deployment distributions may differ from the original setting. The taxonomy reveals several main branches: works handling dual distribution shift (where both class-conditional distributions change), alternative formulations for outlier detection that relax or reframe the Neyman-Pearson constraint, methods ensuring robustness under covariate shift alone, domain adaptation approaches that preserve Neyman-Pearson optimality across domains, and specialized applications in areas like underwater imaging or medical diagnosis. Representative works such as Minimax NP Meta[3] and Generalized NP Domain[5] illustrate how researchers balance theoretical guarantees with practical adaptation, while Transfer NP Outlier[1] and Supervised Outlier Transfer[2] explore connections to outlier detection frameworks that share similar asymmetric loss structures. A particularly active line of work focuses on dual distribution shift, where both the feature distribution and class-conditional densities evolve between source and target. This setting poses unique challenges because classical Neyman-Pearson guarantees no longer hold without careful recalibration. NP Distribution Shift[0] sits squarely within this branch, emphasizing adaptive transfer mechanisms that provide dual-shift guarantees—ensuring that type-I error constraints remain valid even when both distributions change. This contrasts with approaches like Generalized NP Domain[5], which primarily address covariate shift, and Minimax NP Meta[3], which adopts a meta-learning perspective to handle distribution families rather than individual shifts. The central tension across these branches involves trading off between strong theoretical guarantees (often requiring restrictive assumptions) and flexible adaptation strategies that work in practice but may lack formal coverage. NP Distribution Shift[0] contributes to resolving this tension by developing methods that explicitly account for dual shifts while maintaining rigorous error control.

Claimed Contributions

Adaptive transfer learning procedure for Neyman-Pearson classification under both null and alternative distribution shifts

The authors propose an adaptive transfer learning method for Neyman-Pearson classification that handles distribution shifts in both class-0 (null) and class-1 (alternative) distributions. The procedure adaptively leverages source data to improve both Type-I and Type-II errors when the source is informative, while avoiding negative transfer when the source is uninformative, without requiring prior knowledge of source-target relatedness.

5 retrieved papers
Can Refute
Statistical guarantees for transfer learning under general distribution shifts

The authors establish theoretical guarantees for their transfer learning procedure that control both Type-I and Type-II errors simultaneously under distribution shifts in both classes. These guarantees generalize prior work that only addressed shifts in the class-1 distribution, and they introduce transfer moduli to characterize how source performance translates to target performance.

10 retrieved papers
Computational guarantee via reduction to convex programs with bounded gradient complexity

The authors reformulate the learning procedure as a sequence of constrained convex optimization problems and develop a stochastic gradient-based algorithm. They prove that this algorithm achieves the statistical guarantees with polynomial-time gradient complexity, providing both statistical and computational efficiency.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Adaptive transfer learning procedure for Neyman-Pearson classification under both null and alternative distribution shifts

The authors propose an adaptive transfer learning method for Neyman-Pearson classification that handles distribution shifts in both class-0 (null) and class-1 (alternative) distributions. The procedure adaptively leverages source data to improve both Type-I and Type-II errors when the source is informative, while avoiding negative transfer when the source is uninformative, without requiring prior knowledge of source-target relatedness.

Contribution

Statistical guarantees for transfer learning under general distribution shifts

The authors establish theoretical guarantees for their transfer learning procedure that control both Type-I and Type-II errors simultaneously under distribution shifts in both classes. These guarantees generalize prior work that only addressed shifts in the class-1 distribution, and they introduce transfer moduli to characterize how source performance translates to target performance.

Contribution

Computational guarantee via reduction to convex programs with bounded gradient complexity

The authors reformulate the learning procedure as a sequence of constrained convex optimization problems and develop a stochastic gradient-based algorithm. They prove that this algorithm achieves the statistical guarantees with polynomial-time gradient complexity, providing both statistical and computational efficiency.

Table of Contents