aiqbench analysis
This arXiv preprint proposes Conveyance, a loss function for classification tasks where classes have structured relationships, such as hierarchies or ordinal scales. It claims to match or beat specialized baselines on hierarchical classification, ordinal regression, and multiple instance learning, but these are abstract-level claims that need checking against the full benchmarks. For ML practitioners, the appeal is encoding class-graph structure without hand-tuning utility matrices or defining joint distributions.
Conveyance: A Versatile Framework for Learning in Structured Class Spaces
arXiv:2605.28420v3 Announce Type: replace Abstract: While machine learning (ML) architectures have evolved rapidly to account for complex data, loss functions like cross-entropy remain mostly structure-agnostic in many real-world applications. However, the "class-symmetric" nature of these standard losses fundamentally limits the ability of ML models to exploit structural relationships between classes, particularly when facing structured noise. We propose Conveyance, a new classification approach and associated loss function tailored to structured class spaces. It allows users to encode graph-like relations between classes without having to define complex joint distributions or manually tune utility matrices. Technically, our loss function operates by maximizing two separate margins over distinct class partitions, while preserving formal properties such as monotonicity and partial convexity. We demonstrate the versatility and effectiveness of our method by applying it to hierarchical classification, ordinal regression, and multiple instance learning. Across these tasks, Conveyance either matches or exceeds the performance of specialized baselines, thereby offering a unified solution for structured class spaces.
Abstract:While machine learning (ML) architectures have evolved rapidly to account for complex data, loss functions like cross-entropy remain mostly structure-agnostic in many real-world applications. However, the "class-symmetric" nature of these standard losses fundamentally limits the ability of ML models to exploit structural relationships between classes, particularly when facing structured noise. We propose Conveyance, a new classification approach and associated loss function tailored to structured class spaces. It allows users to encode graph-like relations between classes without having to define complex joint distributions or manually tune utility matrices. Technically, our loss function operates by maximizing two separate margins over distinct class partitions, while preserving formal properties such as monotonicity and partial convexity. We demonstrate the versatility and effectiveness of our method by applying it to hierarchical classification, ordinal regression, and multiple instance learning. Across these tasks, Conveyance either matches or exceeds the performance of specialized baselines, thereby offering a unified solution for structured class spaces.
Machine Learning (cs.LG)
arXiv:2605.28420 [cs.LG]
(or arXiv:2605.28420v3 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.28420 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 246 words
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