Adversarially Robust Control of Conditional Value-at-Risk via Rockafellar-Uryasev Conformal Inference
arXiv:2606.00320v1 Announce Type: new Abstract: We present an online, distribution-free framework for controlling the Conditional Value-at-Risk (CVaR), extending conformal tail risk control to non-stationary and adversarial environments. Unlike classical risk control methods, which rely on stationarity or linearity of expectation, our approach provides provable safety guarantees for a nonlinear tail risk functional under arbitrary data-generating processes that may drift or shift strategically over time. By leveraging deep connections between conformal tail risk control, online learning, and t
The increasing complexity and risk in AI-driven systems necessitate more robust methods for online risk control, moving beyond static environments.
This research provides a foundational framework for provably safe and adaptable AI control systems, crucial for deployment in critical applications where risk assessment is paramount.
Traditional risk control methods are being supplanted by adaptive, distribution-free approaches capable of handling non-stationary and adversarial data, enhancing AI system reliability.
- · AI safety researchers
- · Financial services (risk management)
- · Autonomous systems developers
- · Organizations deploying AI in high-stakes environments
- · Developers relying solely on classical risk control methods
- · Systems unprepared for adversarial data shifts
Improved safety and reliability of AI systems in dynamic environments.
Faster adoption of AI in sectors with high regulatory and safety requirements due to enhanced risk management capabilities.
Potential for new regulatory frameworks and compliance standards to incorporate advanced online risk control methodologies.
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Read at arXiv cs.LG