Corollary 4.4: uniform convergence with m^UC_H implies agnostic PAC learnability by ERM with m_H(ε, δ) ≤ m^UC_H(ε/2, δ)
ProvedUnderstandingML.uc_implies_agnostic_pacagnostic-pacermuniform-convergence
Corollary 4.4. If a class has the uniform convergence property with a function then the class is agnostically PAC learnable with the sample complexity . Furthermore, in that case, the paradigm is a successful agnostic PAC learner for .
Formally: if has the uniform convergence property with , then every ERM learner for satisfies the agnostic PAC guarantee (Definition 3.4) with the sample-complexity function , and is agnostic PAC learnable provided an ERM learner exists.
Preamble
import Definitions.Def_UnderstandingML_Framework open MeasureTheory
Formal statement
namespace UnderstandingML
/-- **Corollary 4.4** (p. 55). If a class `H` has the uniform convergence property with a function
`m^{UC}_H`, then the class is agnostically PAC learnable with the sample complexity
`m_H(ε, δ) ≤ m^{UC}_H(ε/2, δ)`; furthermore, in that case the `ERM_H` paradigm is a successful
agnostic PAC learner for `H`: every ERM learner satisfies the agnostic PAC guarantee with the
function `(ε, δ) ↦ m^{UC}_H(ε/2, δ)`, and `H` is agnostic PAC learnable as soon as an ERM learner
exists. -/
theorem uc_implies_agnostic_pac {Z : Type*} [MeasurableSpace Z] {Hyp : Type*}
(loss : Hyp → Z → ℝ) (H : Set Hyp) (mUC : ℝ → ℝ → ℕ)
(hUC : HasUniformConvergenceWith loss H mUC) :
(∀ A : Learner Z Hyp, IsERMLearner loss H A →
IsAgnosticPACWith loss H A (fun ε δ ↦ mUC (ε / 2) δ)) ∧
((∃ A : Learner Z Hyp, IsERMLearner loss H A) → AgnosticPACLearnable loss H) := by sorry
end UnderstandingML
Source
Shalev-Shwartz and Ben-David, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press 2014, doi:10.1017/CBO9781107298019, §4.1 p. 55, Corollary 4.4 (from Lemma 4.2 and Definition 4.3)
Human review
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.