uncertainty

Gaussian Processes give predictions with principled uncertainty, which is why they power Bayesian optimization. The signal is the distribution-over-functions intuition, the kernel's role, and naming the O(n cubed) wall. Here is the answer.

Wrap any model and get prediction sets with a provable coverage rate, no distributional assumptions required. The signal is the calibration-set plus nonconformity-score mechanism and exactly what the guarantee does and does not promise. Here is the answer.

Models hand back confident answers on inputs unlike anything they trained on, which is how silent production failures happen. The signal is knowing why raw softmax confidence lies and which detectors actually separate in- from out-of-distribution.