Game-Theoretic Infrastructure for Online Learning #
Definitions and interface lemmas for the online learning game:
LTree: depth-indexed complete Littlestone treesLTree.isShattered: path-wise shatteringLittlestoneDim: Littlestone dimension asWithBot (WithTop ℕ)OnlineLearner.mistakesFrom: mistake counting from arbitrary stateadversary_core: core adversary lemmaversionSpace,SOA: Standard Optimal Algorithm and its interface- Version-space infrastructure lemmas
Characterization theorems live in FLT_Proofs.Theorem.Online.
Path-wise shattering for complete trees. Path B: leaf case requires C.Nonempty (NA₁₀).
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- LTree.isShattered C LTree.leaf = Set.Nonempty C
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Helper: shattering implies the concept class is nonempty.
Shattering is upward-monotone in the concept class.
Littlestone dimension: the maximum depth of a complete shattered tree. Path B: returns WithBot (WithTop ℕ) so Ldim(∅) = ⊥ (NA₁₀).
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- LittlestoneDim X C = ⨆ (n : ℕ), ⨆ (_ : ∃ (T : LTree X n), LTree.isShattered C T), ↑↑n
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Count mistakes starting from state s.
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- L.mistakesFrom s c [] = 0
- L.mistakesFrom s c (x_1 :: xs) = (if L.predict s x_1 ≠ c x_1 then 1 else 0) + L.mistakesFrom (L.update s x_1 (c x_1)) c xs
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Core adversary lemma.
Relate mistakesFrom to the original mistakes function.
The Standard Optimal Algorithm (SOA).
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SOA prediction: picks the label whose version space side has higher Ldim. NOT @[simp]: proofs should explicitly opt-in to see SOA internals (Inv-stability).
SOA init state is empty history.
SOA mistakesFrom cons: unfold one step using the interface.
Version space subset.
Target stays in version space.
Extending history restricts the version space.
Ldim of version space ≤ Ldim of C.