arXiv:2605.21060v1 Announce Type: new Abstract: Accurate and well-calibrated Machine Learning (ML) models are mandatory in high-stakes settings, yet effective multiclass calibration remains challenging: global approaches assume calibration errors are homogeneous across the latent space, while local methods often rely on latent-space dimensionality reduction, which leads to information loss. To address these issues, we propose a compositional approach to multiclass calibration, where region-specific calibration maps are constructed from shared codeword-dependent factors. We instantiate this ide
The increasing deployment of ML models in critical applications necessitates higher assurance methods for their reliability and trustworthiness, driving research into calibration techniques.
Improved multiclass calibration directly addresses a significant limitation in complex AI systems, enhancing their dependability and trustworthiness, which is crucial for high-stakes deployments.
This research introduces a novel, compositional approach to multiclass calibration, offering a more effective way to ensure model reliability compared to existing global or local methods.
- · AI developers
- · High-stakes AI industries
- · Safety-critical systems development
- · Developers relying on uncalibrated models
- · Sectors experiencing AI deployment failures due to poor calibration
More reliable and trustworthy AI models will be deployed in complex, real-world scenarios.
Increased trust in AI systems may accelerate their adoption in hesitant industries, leading to new applications and markets.
Enhanced AI reliability could pave the way for more autonomous decision-making systems in critical infrastructure and defense.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG