arXiv:2606.01557v1 Announce Type: new Abstract: Everywhere learning is a new paradigm whereby Artificial Intelligence (AI) systems are trained to satisfy loss constraints with probability one over the data distribution. This is in contrast to the standard paradigm of training AI systems to minimize average losses. We develop an approximate duality theory to substantiate a generalization analysis that establishes the proximity between solutions of empirical and statistical everywhere learning problems. Our results show that dual variables reweigh the data distribution towards points in which lo
This paper, published on arXiv, introduces a novel AI training paradigm that addresses limitations of current AI systems, representing a foundational shift in machine learning theory.
The 'everywhere learning' paradigm has the potential to significantly improve the robustness and reliability of AI systems by ensuring loss constraints are met with probability one, moving beyond average loss minimization.
This new paradigm changes the fundamental objective function for AI training, potentially leading to AI systems that are more predictable and trustworthy in critical applications.
- · AI researchers
- · Developers of safety-critical AI
- · Industries requiring high-assurance AI
- · AI systems with high uncertainty
- · Current standard AI training methodologies
Improved performance and reliability of AI models in applications where rare events or specific constraints are critical.
Accelerated adoption of AI in highly regulated industries due to enhanced predictability and verifiability.
The development of entirely new AI applications previously deemed too risky due to current AI's probabilistic guarantees.
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Read at arXiv cs.LG