arXiv:2606.14648v1 Announce Type: new Abstract: Robust machine learning and optimization rely on the uncertainty model choice. We investigate which uncertainty directions a model must cover when defined by a finite dictionary and a budget constraint. Selecting a subset forms an atomic uncertainty set with a closed form support function, yielding tractable robust programs for affine objectives. We propose a data driven selection rule based on a coverage objective over evaluation directions, including gradients, adversarial perturbations, or shifts observed on held out data. We prove this object
The increasing complexity and deployment of AI systems necessitate more robust and reliable uncertainty quantification methodologies.
This research provides a novel method for designing more efficient and tractable robust optimization models, crucial for the trustworthy deployment of AI in critical applications.
The ability to strategically select uncertainty directions will lead to more computationally efficient and theoretically sound robust AI models, reducing the overhead of ensuring model reliability.
- · AI developers
- · High-stakes AI applications (e.g., autonomous systems, finance)
- · Robust optimization researchers
- · Inefficient robust optimization methods
- · Systems highly sensitive to adversarial perturbations if robust design isn't ado
More robust and deployable AI/ML models in various sensitive domains.
Increased trust and adoption of AI technologies due to enhanced reliability and safety.
Potentially reduced regulatory hurdles for AI systems that can rigorously demonstrate robustness.
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Read at arXiv cs.LG