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RemarkRemark: AI-adaptedProof: AI-generatedPipeline-generatedprecheck passjudge pass (gpt-6-luna)
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Exact labels, slow version-space shrinkage, and abstention limits

Remark

The invariant requires the pointwise exact-oracle assumption; standard distributional zero-risk realizability alone need not prescribe labels at pool points of zero input probability. A noisy response can remove the true target from the version space. The ascending-scan query count has no uniform bound by a constant times log⁡∣H∣ over all finite classes; the conditional branch-shrink hypothesis in the query theorem is essential for its logarithmic bound. For fresh features, abstention probability is DX(DIS⁡(V)), which can equal one even when ∣V∣=2. No VC-dimension, disagreement-coefficient, or distribution-free active label-complexity rate is asserted; log⁡ is the natural logarithm.

Remark references

Finite pool-based realizable active-learning instance: states the pointwise exact-oracle convention and defines the pool model.

Realizable and agnostic PAC learning: defines standard distributional realizability by zero population risk.

Population and empirical risk: defines population risk as the expected loss under the joint law.

Population and empirical risks are measurable: proves that the population risk is a defined measurable integral in the supervised model.

Finite-pool version-space query procedure: gives the fixed ascending scan and exact-label version-space update.

Finite-pool version-space invariant and query bound: proves target retention, the finite query bound, and the conditional logarithmic branch-shrink bound.

Unanimous version-space prediction and disagreement abstention: gives the exact fresh-feature abstention probability and zero answered-point error.

The natural logarithm as the inverse of the exponential function: defines the natural logarithm on positive reals.

Order, continuity, range, and the product, quotient, and reciprocal laws for the natural logarithm: supplies the monotonicity, product, and reciprocal laws used in the finite growth comparison.

Steve Hanneke, Theory of Active Learning, version 1.1, §§2.1–2.2 and §5.1

Daniel Joseph Hsu, Algorithms for Active Learning, Chapter 2 §§2.1–2.2.3

Facts & Assumptions

Given: The exact-target finite-pool model, the ascending-scan procedure, and the standard supervised-learning risk convention.

[F1]

Supervised prediction problems: the joint law is a probability measure on the product of the measurable feature and label spaces.

[F2]

Population and empirical risk: population risk is the integral of loss under that joint law; for zero-one classification loss it is the probability of a prediction error.

[F3]

Population and empirical risks are measurable: for a measurable supervised predictor, the loss is measurable and population risk is a defined integral.

[F4]

Realizable and agnostic PAC learning: distributional realizability means some h∈H has RP(h)=0.

[F5]

Finite pool-based realizable active-learning instance: the exact oracle returns h∗(x) at every queried point, and DX is the law for fresh features.

[F6]

Finite-pool version-space query procedure: the procedure scans pool indices in order and removes classifiers that disagree with each queried exact label.

[F7]

Finite-pool version-space invariant and query bound: the target remains in every current version space; the shrink-factor query bound applies only under its stated branch-size hypothesis.

[F8]

Unanimous version-space prediction and disagreement abstention: for a fresh independent feature, the rule abstains exactly on DIS⁡(V) and makes no error when it answers.

[F9]

The natural logarithm as the inverse of the exponential function: for x>0, log⁡x is the unique real y satisfying exp⁡(y)=x.

[F10]

Order, continuity, range, and the product, quotient, and reciprocal laws for the natural logarithm: log⁡ is strictly increasing, satisfies log⁡(xy)=log⁡x+log⁡y for positive x,y, and has log⁡1=0.

[A1]

All witnesses are explicit finite sets and probability laws; no choice axiom or IID sampling assumption is used.

Proof

1.1A1F1F2F3F4F5construct

Let X={a,b} with its full sigma-algebra and label set {0,1} with its discrete sigma-algebra. Take the joint law P=δ(a,0), use zero-one loss ℓ(y^,y)=1{y^≠y}, and set DX=PX=δa, where PX is the input marginal of P. Define H={h0,h1} by h0(a)=h0(b)=0 and h1(a)=0, h1(b)=1. For pool U=(b), choose target h∗=h1 and exact oracle O=h1. The zero-one error events are measurable by [F1] and [F3], and [F2] defines their risks as their P-probabilities. Both risks are zero, so [F4] declares the law realizable by either classifier. Yet h0 and h∗ give different labels at the pool point b, whose DX-probability is zero. Thus distributional realizability does not prescribe the label at every possible pool point; [F5]'s pointwise oracle condition is a genuinely stronger interface.

1.2F6construct

Let X={a}, H={h0,h1} with h0(a)=0 and h1(a)=1, target h∗=h0, and budget b≥1. If the filtering update is fed the noisy response 1 at the initial disagreement a, [F6] replaces V={h0,h1} by {h1}, deleting the true target. Hence the invariant proof cannot be extended to arbitrary noisy responses.

1.3F5F6construct

For any m≥2, take X={x1,…,xm}, target h∗(xj)=1, alternatives hi(xj)=0 exactly when j=i, pool (x1,…,xm), and budget b≥m. Before query i, the version space is {h∗,hi,…,hm}: this is true initially and, at xi, the exact positive label removes only hi. Thus all m indices are queried and q=m=∣H∣−1.

2.1F7F9F10step 1.3algebra

Set m=2k−1 for integers k≥2. Then ∣H∣=2k and q=2k−1. By repeated application of [F10]'s product law, log⁡∣H∣=klog⁡2, and strict increase with 2>1 gives log⁡2>log⁡1=0. The ratio q/(klog⁡2) is at least 2k−1/(klog⁡2); the latter is unbounded because its consecutive-term ratio is 2k/(k+1)≥4/3. Therefore no uniform constant multiple of log⁡∣H∣ bounds the query count, while [F7]'s logarithmic guarantee remains valid when its branch-shrink premise holds.

3.1A1F5F8construct∎

Let X={a}, H={h0,h1} with h0(a)=0, h1(a)=1, target h∗=h0, current version space V=H, and DX({a})=1. The initial version space is consistent with the exact-target model. Then DIS⁡(V)=X, so [F8] gives abstention probability DX(DIS⁡(V))=1 although ∣V∣=2. This verifies that finiteness alone does not force the disagreement mass to be small. All witnesses are explicit finite constructions, as stated in [A1].

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