How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Two indistinguishable laws leave error on an unseen atom
Example
Let with the discrete sigma-algebra and feature marginal , and observe only the labeled pair . Under , the labels at are ; under , they are . The common learner input consists of this labeled pair and the full marginal . Let , and let satisfy , selected from that common input, so it is the same under both laws. For , define The expected population risks satisfy .
Two-law diagram
Each panel gives feature, label, and mass. The input at is observed in both panels; the feature masses agree, while only the unobserved label at changes.
diagram failed to render:
\begin{tikzpicture}
\draw (0,0) rectangle (3.5,1.8);
\node at (1.75,1.45) {$P_0$};
\node at (0.9,0.9) {$a:0$};
\node at (2.6,0.9) {$b:0$};
\node at (0.9,0.35) {$1/2$};
\node at (2.6,0.35) {$1/2$};
\draw (4,0) rectangle (7.5,1.8);
\node at (5.75,1.45) {$P_1$};
\node at (4.9,0.9) {$a:0$};
\node at (6.6,0.9) {$b:1$};
\node at (4.9,0.35) {$1/2$};
\node at (6.6,0.35) {$1/2$};
\end{tikzpicture}Facts & Assumptions
Given: The finite feature set , common masses at each point, observed label 0 at , and the two deterministic label laws in the Example.
The unlabeled marginal alone does not identify labels: for an unseen atom of mass , the maximum expected risk under two label laws differing only at that atom is at least for any common predictor distribution.
Population and empirical risk: population risk is the expected loss under the joint law; for the finite zero-one laws in this Example it is the sum of each atom mass times its error probability.
Verification
Here the unseen atom is and . The two laws agree at the observed point and have the same , so the theorem in [F1] applies and gives a maximum expected risk of at least .
Directly, let . The two expected risks have the same nonnegative contribution from . Their contributions at are respectively under and under , so their sum is at least and their maximum is at least .
Depends on
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
4 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.