Conveyance: A Versatile Framework for Learning in Structured Class Spaces

2026-05-27

AI Summary

This work addresses the limitation of conventional loss functions—such as cross-entropy—in neglecting structural relationships among classes, which hinders their ability to handle structured label noise or incorporate prior knowledge. The authors propose Conveyance, a novel framework that introduces, for the first time, a unified loss function capable of jointly addressing tasks with structured label spaces, including hierarchical classification, ordinal regression, and multiple-instance learning. By modeling class relationships through a graph structure, the method avoids the need for complex joint distributions or manually designed utility matrices. It further incorporates a double-margin maximization mechanism to optimize decision margins across varying class partitions. The proposed loss enjoys favorable theoretical properties, such as monotonicity and partial convexity, and achieves performance on par with or superior to task-specific methods across multiple benchmark datasets, demonstrating its generality and effectiveness.

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 \textsc{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, \textsc{Conveyance} either matches or exceeds the performance of specialized baselines, thereby offering a unified solution for structured class spaces.

Problem

• structured class spaces
• loss function
• class relationships
• structured noise
• classification

Innovation

• structured class spaces
• Conveyance
• graph-based class relations
• margin-based loss
• unified classification framework