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Fairness-Aware Multi-view Evidential Learning with Adaptive Prior

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.5Download Report PDF
multi-view evidential learninguncertainty estimation
Abstract:

Multi-view evidential learning aims to integrate information from multiple views to improve prediction performance and provide trustworthy uncertainty estimation. Most previous methods assume that view-specific evidence learning is naturally reliable. However, in practice, the evidence learning process tends to be biased. Through empirical analysis on real-world data, we reveal that samples tend to be assigned more evidence to support data-rich classes, thereby leading to unreliable uncertainty estimation in predictions. This motivates us to delve into a new Biased Evidential Multi-view Learning (BEML) problem. To this end, we propose Fairness-Aware Multi-view Evidential Learning (FAML). FAML first introduces an adaptive prior based on training trajectories, which acts as a regularization strategy to flexibly calibrate the biased evidence learning process. Furthermore, we explicitly incorporate a fairness constraint based on class-wise evidence variance to promote balanced evidence allocation. In the multi-view fusion stage, we propose an opinion alignment mechanism to mitigate view-specific bias across views, thereby encouraging the integration of consistent and mutually supportive evidence. Theoretical analysis shows that FAML enhances fairness in the evidence learning process. Extensive experiments on six real-world multi-view datasets demonstrate that FAML achieves more balanced evidence allocation and improves both prediction performance and the reliability of uncertainty estimation compared to state-of-the-art methods.

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 addresses biased evidence allocation in multi-view evidential learning, proposing adaptive priors and fairness constraints to calibrate uncertainty estimation. According to the taxonomy, it occupies the 'Adaptive Prior-Based Evidence Regularization' leaf under 'Fairness-Aware Evidential Learning with Adaptive Calibration'. Notably, this leaf contains only the original paper itself with no sibling papers, suggesting this specific combination of adaptive priors and fairness-aware evidence regularization represents a relatively unexplored research direction within the broader multi-view learning landscape.

The taxonomy reveals a sparse field structure with only two main branches and two leaf nodes total. The neighboring branch focuses on 'Graph-Based Confidence Calibration Under Adversarial Conditions', which addresses robustness in graph neural networks rather than general multi-view settings. The scope notes clarify that methods using consistency regularization without adaptive priors belong elsewhere, while the original paper's approach of training-trajectory-based adaptive priors distinguishes it from consistency-based calibration strategies. This positioning suggests the work bridges fairness concerns with evidential uncertainty in a relatively novel way.

Among 28 candidates examined, the contribution-level analysis reveals mixed novelty signals. The BEML problem formulation examined 10 candidates with none appearing to refute it, suggesting this framing may be relatively fresh. However, the training-trajectory-based adaptive prior mechanism examined 10 candidates and found 1 refutable match, indicating some overlap with existing adaptive prior work. The opinion alignment mechanism for multi-view fusion examined 8 candidates with no refutations, suggesting this fusion strategy may offer more distinctive technical contributions within the limited search scope.

Based on the top-28 semantic matches examined, the work appears to occupy a sparse research area with limited direct competition in its specific leaf. The taxonomy structure and contribution-level statistics suggest moderate novelty, though the analysis acknowledges its limited scope—a more exhaustive literature search across multi-view learning, evidential deep learning, and fairness-aware machine learning might reveal additional related work not captured in this focused examination.

Taxonomy

Core-task Taxonomy Papers
1
3
Claimed Contributions
28
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: Mitigating evidential bias in multi-view learning through fairness-aware uncertainty estimation. This field addresses the challenge of ensuring that multi-view learning systems produce fair and well-calibrated uncertainty estimates, particularly when different data views may introduce or amplify biases. The taxonomy reveals two main branches: one focused on fairness-aware evidential learning with adaptive calibration, which emphasizes methods that adjust evidence collection and uncertainty quantification to reduce bias across subgroups, and another centered on graph-based confidence calibration under adversarial conditions, which explores robustness and calibration in structured data settings where adversarial perturbations or distributional shifts may occur. These branches reflect complementary concerns—one prioritizing equitable treatment across views and demographic groups, the other emphasizing resilience to challenging or hostile data conditions. Within the fairness-aware branch, a key theme is the development of adaptive mechanisms that regularize evidence based on learned priors, allowing models to dynamically balance uncertainty across views without over-relying on potentially biased sources. Fairness Evidential Learning[0] exemplifies this direction by introducing adaptive prior-based evidence regularization, aiming to ensure that uncertainty estimates remain fair even when individual views carry systematic biases. This approach contrasts with earlier work such as Fair Graph Representation[1], which addresses fairness in graph-structured data but does not explicitly incorporate evidential uncertainty frameworks. The central trade-off in this area involves maintaining high predictive performance while ensuring that uncertainty quantification does not disproportionately penalize underrepresented groups or views, a challenge that remains an active area of exploration.

Claimed Contributions

Biased Evidential Multi-view Learning (BEML) problem formulation

The authors identify and formalize a previously neglected problem in multi-view evidential learning where the evidence learning process exhibits implicit unfairness, with samples tending to receive more evidence for data-rich classes, leading to unreliable uncertainty estimation.

10 retrieved papers
Training-trajectory-based adaptive prior mechanism

The authors propose an adaptive prior mechanism that adjusts Dirichlet distribution parameters based on class-wise performance during training, providing compensatory support to poorly performing classes and promoting balanced evidence allocation across different classes.

10 retrieved papers
Can Refute
Opinion alignment mechanism for multi-view fusion

The authors design a mechanism that minimizes discrepancies between view-specific opinions during the fusion stage, ensuring different views align on both predictions and confidence levels to reduce view-specific bias in the final decision.

8 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

Biased Evidential Multi-view Learning (BEML) problem formulation

The authors identify and formalize a previously neglected problem in multi-view evidential learning where the evidence learning process exhibits implicit unfairness, with samples tending to receive more evidence for data-rich classes, leading to unreliable uncertainty estimation.

Contribution

Training-trajectory-based adaptive prior mechanism

The authors propose an adaptive prior mechanism that adjusts Dirichlet distribution parameters based on class-wise performance during training, providing compensatory support to poorly performing classes and promoting balanced evidence allocation across different classes.

Contribution

Opinion alignment mechanism for multi-view fusion

The authors design a mechanism that minimizes discrepancies between view-specific opinions during the fusion stage, ensuring different views align on both predictions and confidence levels to reduce view-specific bias in the final decision.

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