Appendix A.0.1, Claim — ℙ(X ∈ B) = p_B for X ∼ 𝒩(x, σ²I)
ProvedCohen2019.Tight.prob_X_mem_Bgaussianp2o-batch-pfp1ap2o-gran-per-chapterp2o-plan-paperp2o-v1randomized-smoothing
Let , , with , and . Let and . Then
The half-space is calibrated so that the worst-case classifier puts exactly the allowed mass on the runner-up class .
Formalization Note. and are added. The paper's proof recalls with "" and writes in its first line; both are misprints, and is the set defined on p. 14.
Preamble
import Mathlib import Definitions.Def_Cohen2019_Tight_Model import Definitions.Def_Cohen2019_Tight_HalfSpaces open MeasureTheory ProbabilityTheory
Formal statement
namespace Cohen2019.Tight
/-- Cohen, Rosenfeld, Kolter, arXiv:1902.02918v2, Appendix A.0.1, Claim `ℙ(X ∈ B) = p̄B`, p. 15:
for `X ∼ 𝒩(x, σ²I)`, the half-space `B = {z : δᵀ(z − x) ≥ σ‖δ‖Φ⁻¹(1 − p̄B)}` has probability
`p̄B`. (The proof on p. 15 recalls `B` with `≤` and writes `ℙ(X ∈ A)` in its first line; both
are misprints, and `B` is the set defined on p. 14.)
**Formalization Note.** The hypotheses `δ ≠ 0` and `0 < p̄B < 1` are added, as for
`prob_X_mem_A`. -/
theorem prob_X_mem_B {d : ℕ} (x δ : Space d) (σ pB : ℝ) (hσ : 0 < σ) (hδ : δ ≠ 0)
(hpB0 : 0 < pB) (hpB1 : pB < 1) :
(gaussNoise x σ (setB x δ σ pB)).toReal = pB := by sorry
end Cohen2019.Tight
Source
Cohen, Rosenfeld, Kolter, Certified Adversarial Robustness via Randomized Smoothing, arXiv:1902.02918v2, Appendix A.0.1, Claim ℙ(X ∈ B) = p_B, p. 15
Human review
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.