Jethro's Braindump

Rademacher Complexity

In PAC Learning, We have shown that uniform convergence is a sufficient condition for learnability. Rademacher complexity measures the rate of uniform convergence. Rademacher complexity can also be used to provide generalization bounds.


Let us denote:

\begin{equation} \mathcal{F} \overset{\mathrm{def}}{=} l \circ \mathcal{H} \overset{\mathrm{def}}{=} \left\{ z \rightarrow l(h,z) : h \in \mathcal{H} \right\} \end{equation}

given \(f \in \mathcal{F}\), we also define:

\begin{equation} L_D(f) = \mathbb{E}_{z \sim D} \left[ f(z) \right], L_S(f) = \frac{1}{m} \sum_{i=1}^{m} f(z_i) \end{equation}

We define the representativeness of \(S\) with respect to \(\mathcal{F}\) as the largest gap between the true error of a function \(f\), and its empirical error:

\begin{equation} \mathrm{Rep}_D(\mathcal{F}, S) \overset{\mathrm{def}}{=} \mathrm{sup}_{f \in \mathcal{F}} (L_D(f) - L_S(f)) \end{equation}

Suppose we would like to estimate the representativeness of \(S\) using the sample \(S\) only. One simple idea is to split \(S\) into 2 disjoint sets, \(S = S_1 \cup S_2\) ; refer to \(S_1\) as the validation set and \(S_2\) as the training set. We can then estimate the representativeness of \(S\) by:

\begin{equation} \mathrm{sup}_{f \in \mathcal{F}} (L_{S_1}(f) - L_{S_2}(f)) \end{equation}

If we define \(\mathbf{\sigma} = (\sigma_1, \dots, \sigma_m) \in \left\{ \pm 1\right\}^m\), to be a vector such that \(S_1 = \{ z_i : \sigma_i = 1\}\) and \(S_2 = \{ z_i : \sigma_i = -1\}\). If we further assume \(|S_1| = |S_2|\), then:

\begin{equation} \frac{2}{m} \mathrm{sup}_{f \in \mathcal{F}} \sum_{i=1}^{m} \sigma_i f(z_i) \end{equation}

The Rademacher complexity measure captures this idea by considering the expectation of the above with respect to a random choice of \(\mathcal{\sigma}\). Formally, let \(\mathcal{F} \circ S\) be the set of all possible evaluations a function \(f \in \mathcal{F}\) can achieve on sample S, namely:

\begin{equation} \mathcal{F} \circ S = \left\{ (f(z_1), \dots, f(z_m)) : f \in \mathcal{F} \right\} \end{equation}

Let the variables in \(\mathbf{\sigma}\) be distributed i.i.d. according to \(\mathbb{P}[\sigma_i = 1] = \mathbb{P}[\sigma_i = -1] = \frac{1}{2}\). Then the Rademacher complexity of \(\mathcal{F}\) with respect to \(S\) is defined as:

\begin{equation} R(\mathcal{F} \circ S) \overset{def}{=} \frac{1}{m} \mathbb{E}_{\mathbf{\sigma} \in \{ \pm 1\}^m} \left[ \mathrm{sup}_{f \in \mathcal{F}} \sum_{i=1}^{m} \sigma_i f(z_i) \right] \end{equation}

More generally, given a set of vectors \(A \subset \mathbb{R}^m\), we define

\begin{equation} R(A) \overset{\mathrm{def}}{=} \frac{1}{m} \mathbb{E}_{\mathbf{\sigma}} \left[ \mathrm{sup}_{f \in \mathcal{F}} \sum_{i=1}^{m} \sigma_i f(z_i) \right] \end{equation}

The following lemma bounds the expected value of the representativeness of \(S\) by twice the expected Rademacher complexity.

\begin{equation} \mathbb{E}_{S \sim \mathcal{D}^m} \left[ \mathrm{Rep}_{\mathcal{D}} (\mathcal{F}, S) \right] \le 2 \mathbb{E}_{S \sim \mathcal{D}^m} R(\mathcal{F} \circ S) \end{equation}

This lemma yields that, in expectation, the ERM rule finds a hypothesis which is close to the optimal hypothesis in \mathcal{H}.

\begin{equation} \mathbb{E}_{S \sim \mathcal{D}^m} \left[ L_D(ERM_{\mathcal{H}}(S)) - L_S(ERM_{\mathcal{H}}(S))\right] \le 2 \mathbb{E}_{S \sim \mathcal{D}^m} (l \circ \mathcal{H} \circ S) \end{equation}

Furthermore, for any \(h^* \in \mathcal{H}\)

\begin{equation} \mathbb{E}_{S \sim \mathcal{D}^m} \left[ L_D(ERM_{\mathcal{H}}(S)) - L_D(h^*)\right] \le 2 \mathbb{E}_{S \sim \mathcal{D}^m} (l \circ \mathcal{H} \circ S) \end{equation}

Furthermore, if \(h^* = \mathrm{argmin}_h L_{\mathcal{D}}(h)\) then for each \(\delta \in (0,1)\) with probability of at least \(1 - \delta\) over the choice of \(S\), we have:

\begin{equation} L_{\mathcal{D}} (ERM_{\mathcal{H}}(S) - L_{\mathcal{D}}(h^*)) \le \frac{2 \mathbb{E}_{S’ \sim \mathcal{D}^m} R(l \circ \mathcal{H} \circ S’)}{\delta} \end{equation}

Using McDiarmid’s Inequality, we can derive bounds with better dependence on the confidence parameter:

Assume that for all \(z\) and \(h \in \mathcal{H}\) we have that \(|l(h,z) \le c|\). Then,

  1. With probability at least \(1 - \delta\), for all \(h \in \mathcal{H}\),

\begin{equation} L_{\mathcal{D}} (h) - L_S(h) \le 2 \mathbb{E}_{S’ \sim \mathcal{D}^m} R(l \circ \mathcal{H} \circ S’) + c \sqrt{\frac{2 \ln(2/\delta)}{m}} \end{equation}

In particular, this holds for \(h = ERM_{\mathcal{H}}(S)\).

  1. With probability at least \(1 - \delta\), for all \(h \in \mathcal{H}\),

\begin{equation} L_{\mathcal{D}} (h) - L_S(h) \le 2 R(l \circ \mathcal{H} \circ S) + 4c\sqrt{\frac{2 \ln(4/\delta)}{m}} \end{equation}

  1. For any \(h^*\) , with probability at least \(1 - \delta\),

\begin{equation} L_{\mathcal{D}} (ERM_{\mathcal{H}} (S)) - L_D(h^*) \le 2 R(l \circ \mathcal{H} \circ S) + 5c\sqrt{\frac{2 \ln(8/\delta)}{m}} \end{equation}

Massart’s lemma states that the Rademacher complexity of a finite set grows logarithmically with the size of the set.

Let \(A = \{a_1, \dots, a_N\}\) be a finite set of vectors in \(\mathbb{R}^m\). Define \(\bar{a} = \frac{1}{N} \sum_{i=1}^N a_i\). Then,

\begin{equation} R(A) \le \mathrm{max}_{a \in A} \lVert a - \bar{a} \rVert \frac{\sqrt{2 \log(N)}}{m} \end{equation}

The contraction lemma shows that composing \(A\) with a Lipschitz function does not blow up the Rademacher complexity.

Rademacher complexity of linear classes

We define 2 linear classes:

\begin{equation} \mathcal{H}_1 = \left\{x \rightarrow \langle w,x \rangle : \lVert w \rVert_1 \le 1\right\} \end{equation}

\begin{equation} \mathcal{H}_2 = \left\{x \rightarrow \langle w,x \rangle : \lVert w \rVert_2 \le 1\right\} \end{equation}

\(\mathcal{H}_2\) is bounded by the following lemma:

Let \(S = (x_1, \dots, x_m)\) be vectors in an Hilbert space. Define \(\mathcal{H}_2 \circ S = \left\{( \langle w, x_1 \rangle), \langle w, x_2 \rangle), \dots, \langle w, x_m \rangle) : \lVert w \rVert_2 \le 1 \right\}\). Then,

\begin{equation} R(\mathcal{H}_2 \circ S) \le \frac{\mathrm{max}_i \lVert x_i \rVert_2}{\sqrt{m}} \end{equation}

The following lemma \(\mathcal{H}_1\):

Let \(S = (x_1, \dots, x_m)\) be vectors in \(\mathbb{R}^n\). Then,

\begin{equation} R(\mathcal{H}_1 \circ S) \le \mathrm{max}_i \lVert x_i \rVert_\infty \sqrt{\frac{2 \log(2n)}{m}} \end{equation}