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.
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These labels describe origin, not correctness: citations and verification chips remain separate evidence.
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 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 , which can equal one even when . No VC-dimension, disagreement-coefficient, or distribution-free active label-complexity rate is asserted; 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.
Supervised prediction problems: the joint law is a probability measure on the product of the measurable feature and label spaces.
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.
Population and empirical risks are measurable: for a measurable supervised predictor, the loss is measurable and population risk is a defined integral.
Realizable and agnostic PAC learning: distributional realizability means some has .
Finite pool-based realizable active-learning instance: the exact oracle returns at every queried point, and is the law for fresh features.
Finite-pool version-space query procedure: the procedure scans pool indices in order and removes classifiers that disagree with each queried exact label.
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.
Unanimous version-space prediction and disagreement abstention: for a fresh independent feature, the rule abstains exactly on and makes no error when it answers.
The natural logarithm as the inverse of the exponential function: for , is the unique real satisfying .
Order, continuity, range, and the product, quotient, and reciprocal laws for the natural logarithm: is strictly increasing, satisfies for positive , and has .
All witnesses are explicit finite sets and probability laws; no choice axiom or IID sampling assumption is used.
Proof
Let with its full sigma-algebra and label set with its discrete sigma-algebra. Take the joint law , use zero-one loss , and set , where is the input marginal of . Define by and , . For pool , choose target and exact oracle . The zero-one error events are measurable by [F1] and [F3], and [F2] defines their risks as their -probabilities. Both risks are zero, so [F4] declares the law realizable by either classifier. Yet and give different labels at the pool point , whose -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.
Let , with and , target , and budget . If the filtering update is fed the noisy response at the initial disagreement , [F6] replaces by , deleting the true target. Hence the invariant proof cannot be extended to arbitrary noisy responses.
For any , take , target , alternatives exactly when , pool , and budget . Before query , the version space is : this is true initially and, at , the exact positive label removes only . Thus all indices are queried and .
Set for integers . Then and . By repeated application of [F10]'s product law, , and strict increase with gives . The ratio is at least ; the latter is unbounded because its consecutive-term ratio is . Therefore no uniform constant multiple of bounds the query count, while [F7]'s logarithmic guarantee remains valid when its branch-shrink premise holds.
Let , with , , target , current version space , and . The initial version space is consistent with the exact-target model. Then , so [F8] gives abstention probability although . This verifies that finiteness alone does not force the disagreement mass to be small. All witnesses are explicit finite constructions, as stated in [A1].
Depends on
- Finite-pool version-space query procedure
- The natural logarithm as the inverse of the exponential function
- Finite pool-based realizable active-learning instance
- Population and empirical risk
- Realizable and agnostic PAC learning
- Supervised prediction problems
- Population and empirical risks are measurable
- Finite-pool version-space invariant and query bound
- Unanimous version-space prediction and disagreement abstention
- Order, continuity, range, and the product, quotient, and reciprocal laws for the natural logarithm
Used by
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Sources
- Steve Hanneke, Theory of Active Learning, version 1.1, §§2.1–2.2 and §5.1 (standard reference, not scraped)
- Daniel Joseph Hsu, Algorithms for Active Learning, Chapter 2 §§2.1–2.2.3 (standard reference, not scraped)