arXiv:2605.11490v2 Announce Type: replace Abstract: Making calibrated online predictions is a central challenge in modern AI systems. Much of the existing literature focuses on fully adversarial environments where outcomes may be arbitrary, leading to conservative algorithms that can perform suboptimally in more benign settings, such as when outcomes are nearly stationary. This gap raises a natural question: can we design online prediction algorithms whose calibration error automatically adapts to the degree of non-stationarity in the environment, smoothly interpolating between i.i.d. and adve
The increasing deployment of AI systems in complex, real-world environments necessitates more robust and adaptable prediction methods that can handle varying degrees of data stability.
Improving the calibration of online predictions in non-stationary environments will lead to more reliable and trustworthy AI systems, expanding their applicability in critical domains.
AI systems will become more resilient to real-world data fluctuations, offering more nuanced and less conservative performance compared to current adversarial robustness approaches.
- · AI developers
- · Industries relying on predictive AI
- · Machine learning researchers
- · AI systems with poor calibration
- · Black-box prediction models
Increased trust and adoption of AI systems in dynamic operational settings.
Reduced need for constant human oversight and recalibration of deployed AI models.
Acceleration of autonomous AI agents capable of operating effectively in highly variable and uncertain environments.
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Read at arXiv cs.LG