arXiv:2402.07407v3 Announce Type: replace-cross Abstract: We propose conformal predictive programming (CPP), a framework to solve chance constrained optimization problems, i.e., optimization problems with constraints that are functions of random variables. CPP utilizes samples from these random variables along with the quantile lemma - central to conformal prediction - to transform the chance constrained optimization problem into a deterministic problem with a quantile reformulation. CPP's main strength is an independent calibration step that provides a posteriori guarantees for the solution o
The increasing complexity of AI systems and real-world applications necessitates robust methods for handling uncertainty and ensuring reliability, driving research in areas like constrained optimization.
This development offers a principled approach to integrating probabilistic guarantees into AI decision-making, which is crucial for deployment in safety-critical and high-stakes environments.
The ability to transform complex chance-constrained optimization problems into deterministic ones with posteriori performance guarantees streamlines the development and verification of AI systems.
- · AI developers
- · Robotics
- · Autonomous systems
- · Safety-critical applications
- · Traditional heuristic optimization methods
- · Systems lacking formal uncertainty quantification
More reliable and certifiable AI and autonomous systems can be developed and deployed.
This could accelerate the adoption of AI in applications requiring stringent safety and performance standards.
Increased trust in AI might lead to broader societal integration, potentially impacting regulatory frameworks and liability discussions.
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