arXiv:2607.08103v1 Announce Type: new Abstract: Rank estimation under label noise poses a fundamental challenge, as ordinal annotations often exhibit structured uncertainty rather than simple label corruption. In this paper, we reformulate rank estimation with noisy ordinal labels as a stochastic ordering problem, in which each instance is inherently associated with multiple plausible ranks instead of a single deterministic label. Based on this view, we propose stochastic order learning (SOL), a learning framework that captures ordinal label uncertainty and learns an embedding space through tw
The increasing complexity and scale of AI models necessitate more robust methods for handling data uncertainty, especially in ranking and preference learning tasks.
Improved methods for rank estimation under noise can lead to more reliable AI systems in critical applications, reducing errors and bias originating from imperfect data.
This research provides a novel theoretical framework and practical approach (SOL) to directly address structured uncertainty in ordinal labels, moving beyond simple corruption models.
- · AI researchers
- · Data scientists
- · Machine learning platforms
- · Systems relying on naive rank estimation
- · Inflexible AI models
More accurate and resilient AI systems in applications requiring ranked outputs, such as recommendation engines or medical diagnostics.
Reduced incidence of AI failures or suboptimal performance due to noisy or uncertain human-annotated data.
Potentially enables new applications for AI in domains where ordinal data is inherently uncertain but critical, leading to new market opportunities.
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Read at arXiv cs.LG