# Proof of uniform convergence if VC dimension is finite

In the book »Understanding Machine Learning: From Theory to Algorithms«, written by Ben-David and Shalev-Shwartz, there is a proof which I do not understand. The context is proving that if a hypothesis class $$\mathcal{H}$$ has finite VC dimension, then it enjoys the uniform convergence property.

The proof of this implication involves Sauer's Lemma (hypothesis classes with finite VC dimension have »small effective size«) and a theorem stating that hypothesis classes with »small effective size« enjoy the uniform convergence property. The proof of the latter sub-implication involves this theorem (Theorem 6.11 in the book):

Let $$\mathcal{H}$$ be a hypethesis class of binary classifiers over a domain $$\mathcal{X}$$ and let $$\tau_{\mathcal{H}}$$ be its growth function (from Sauer's Lemma). Then, for all $$m \in \mathbb{N}$$, for every distribution $$\mathcal{D}$$ over $$\mathcal{X} \times \{0, 1\}$$ and every $$\delta \in (0, 1)$$: $$\forall h \in \mathcal{H}: \mathbb{P}_{S \sim \mathcal{D}^m}\left[ | L_{\mathcal{D}}(h) - L_S(h) | \leq \frac{4 + \sqrt{\log(\tau_{\mathcal{H}}(2m))}}{\delta\sqrt{2m}} \right] \geq 1 - \delta$$

In the proof for this theorem it is stated, that it follows immediately from the inequality $$\mathbb{E}_{S \sim \mathcal{D}^m}\left[ \sup_{h \in \mathcal{H}} | L_{\mathcal{D}}(h) - L_S(h) | \right] \leq \frac{4 + \sqrt{\log(\tau_{\mathcal{H}}(2m))}}{\sqrt{2m}},$$ the fact that the random variable $$\theta := \sup_{h \in \mathcal{H}} | L_{\mathcal{D}}(h) - L_S(h) |$$ is nonnegative and Markov's inequality.

However, I do not see how it follows immediately from those facts. What am I missing?

• This is an immediate application of Markov's inequality. Commented Apr 1, 2020 at 11:54

Let $$X = |L_\mathcal{D}(h) - L_S(h)|$$. The statement on the expectation of the supremum of $$X$$ implies, in particular, that for some $$M$$, $$\mathbb{E}[X] \leq M.$$ Since $$X \geq 0$$, Markov's inequality implies that $$\Pr\left[X \geq \frac{M}{\delta}\right] \leq \delta.$$ This implies that $$\Pr\left[X \leq \frac{M}{\delta}\right] \geq \Pr\left[X < \frac{M}{\delta}\right] = 1 - \Pr\left[X \geq \frac{M}{\delta}\right] \geq 1 - \delta.$$