pith:DFAT3OH2
Strategic PAC Learnability via Geometric Definability
A geometric definability assumption ensures strategic classification remains PAC learnable with sample complexity governed by formula complexity.
arxiv:2605.13426 v2 · 2026-05-13 · cs.LG · math.AG
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Record completeness
Claims
Under the geometric definability assumption, learnability is preserved, with sample complexity controlled by the complexity of the defining formulas.
Both the hypothesis class and the cost-induced neighborhood relation can be defined by first-order formulas over R_exp.
Strategic PAC learnability is preserved when hypothesis classes and cost-induced neighborhoods are first-order definable over the reals with exponentiation, controlling sample complexity by formula complexity.
References
Formal links
Receipt and verification
| First computed | 2026-05-18T02:44:47.262227Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
19413db8faf37eb7246e58fda47a457db9ea63f1b0d25584758e925c2bd341e2
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/DFAT3OH26N7LOJDOLD62I6SFPW \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 19413db8faf37eb7246e58fda47a457db9ea63f1b0d25584758e925c2bd341e2
Canonical record JSON
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