Uniform stability implies replace-one stability
ProvedStabGen.Uniform.uniform_stability_replace_onealgorithmic-stabilitylearning-theoryp2o-batch-pfp1ap2o-gran-per-chapterp2o-plan-paperp2o-v1
Let be a symmetric learning algorithm with uniform stability at sample size with respect to the loss (removing any one example changes by at most , for every ). Then for every sample , every index , every replacement point and every ,
where is with replaced by .
Stability with respect to the removal of one point thus implies stability with respect to the change of one point. This is the form of stability used in the bounded-differences step of Theorem 12.
Preamble
import Mathlib import Definitions.Def_FoundationsML_Stability_Loss import Definitions.Def_StabGen_Hypothesis_Setting import Definitions.Def_StabGen_Uniform_Stability open FoundationsML.Stability
Formal statement
namespace StabGen.Uniform
/-- Bousquet & Elisseeff 2002, p. 504 (after Definition 6): an algorithm with uniform stability
`β` at size `m` satisfies `|ℓ(A_S, z) − ℓ(A_{S^i}, z)| ≤ 2β` for every sample `S`, index `i`,
replacement point `z'_i` and point `z`. -/
theorem uniform_stability_replace_one {X Y Y' : Type*} (L : Y' → Y → ℝ)
(A : StabGen.Hypothesis.LearningAlgorithm X Y Y') (m : ℕ) (β : ℝ) (hstab : HasUniformStability L A m β) :
∀ (S : Fin m → X × Y) (i : Fin m) (z' z : X × Y),
|Loss L (A (StabGen.Hypothesis.trainingSet S)) z - Loss L (A (StabGen.Hypothesis.trainingSet (StabGen.Hypothesis.replaceAt S i z'))) z| ≤ 2 * β := by sorry
end StabGen.Uniform
Source
Bousquet & Elisseeff, Stability and Generalization, JMLR 2 (2002), p. 504, remark after Definition 6
Human review
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.