Basic Integrals from Kingma, Welling (2013)

08 Sep 2021

[ variational-inference  ]

In this post I’ll show the calculations behind some integrals presented in the seminal paper “Auto-Encoding Variational Bayes” by Kingma and Welling (2013). The paper presents the results of those integrals, however it does not derive them. I was interested in the derivations and I’m noting them here because someone else may find them useful too. I will not be discussing any other concepts from the paper in this post. We are interested in maximizing the \(\text{ELBO}\):

\[\text{ELBO}(x; \theta, \phi) = \underbrace{\mathbb{E}_{q_\phi(z|x)}\left[\log p(x|z;\theta)\right]}_{\text{Expected Negative} \\ \text{Reconstruction} \\ \text{Error}} - \underbrace{D_{KL}\left(q_\phi(z|x) || p_\theta(z)\right)}_{\text{KL Divergence Between} \\ \text{Variational and} \\ \text{Prior Distributions}}\]

While we use Monte Carlo to estimate the Expected Negative Reconstruction Error, we can analytically compute the KL Divergence term when the prior \(p_\theta(z)\) and the variational distribution \(q_\phi(z\|x^{(i)})\) are Gaussians with a diagonal covariance structure. We want to compute \(D_{KL}\left(q_\phi(z\|x) \|\| p_\theta(z)\right)\) where \(p_\theta(z) = \mathcal{N}(z; 0, \mathbf{I})\) and \(q_\phi(z\|x^{(i)}) = \mathcal{N}(z; \mu^{(i)}, \sigma^{(i)2})\). Let \(J\) be the dimensionality of \(z\). Then:

\[\int q_\phi(z|x^{(i)}) \log p_\theta(z)dz = \int \mathcal{N}(z; \mu^{(i)}, \sigma^{(i)2}) \log \mathcal{N}(z; 0, \mathbf{I}) dz \\ = \int \mathcal{N}(z; \mu^{(i)}, \sigma^{(i)2}) \log \left(\frac{1}{\sqrt{(2\pi)^J}} \exp{\left(-\frac{z^Tz}{2}\right)}\right)dz \\ = - \frac{J}{2}\log {2\pi}\underbrace{\int \mathcal{N}(z; \mu^{(i)}, \sigma^{(i)2}) dz}_{1} - \frac{1}{2} \int \mathcal{N}(z; \mu^{(i)}, \sigma^{(i)2}) z^Tz dz \\ = - \frac{J}{2}\log {2\pi} - \frac{1}{2} \mathbb{E}_{\mathcal{N}(z; \mu^{(i)}, \sigma^{(i)2})}\left[||z||_2^2\right] \\ = - \frac{J}{2}\log {2\pi} - \frac{1}{2} \mathbb{E}_{\mathcal{N}(z; \mu^{(i)}, \sigma^{(i)2})}\left[\sum_jz_j^2\right] \\ = - \frac{J}{2}\log {2\pi} - \frac{1}{2} \sum_j\mathbb{E}\left[z_j^2\right] \\ = - \frac{J}{2}\log {2\pi} - \frac{1}{2} \sum_j\left(\mathbb{Var}\left[z_j\right] + \mathbb{E}\left[z_j\right]^2\right) \\ = - \frac{J}{2}\log {2\pi} - \frac{1}{2} \sum_j\left(\sigma_j^{(i)2} + \mu_j^{(i)2}\right)\]


\[\int q_\phi(z|x^{(i)}) \log q_\phi(z|x^{(i)}) dz = \int \mathcal{N}(z; \mu^{(i)}, \sigma^{(i)2}) \log \mathcal{N}(z; \mu^{(i)}, \sigma^{(i)2}) dz \\ = \int \mathcal{N}(z; \mu^{(i)}, \sigma^{(i)2}) \log \left(\frac{1}{\sqrt{(2\pi)^J\prod_j\sigma_j^2}} \exp{\left(-\frac{(z-\mu)^T(\sigma^2\mathbf{I})^{-1}(z-\mu)}{2}\right)}\right)dz \\ = \left(- \frac{J}{2}\log {2\pi} -\frac{1}{2}\sum_j\log \sigma_j^{(i)2}\right) \underbrace{\int \mathcal{N}(z; \mu^{(i)}, \sigma^{(i)2})dz}_{1} - \frac{1}{2} \int \mathcal{N}(z; \mu^{(i)}, \sigma^{(i)2}) \sum_j \frac{(z_j-\mu_j^{(i)})^2}{\sigma_j^{(i)2}}dz \\ = - \frac{J}{2}\log {2\pi} -\frac{1}{2}\sum_j\log \sigma_j^{(i)2} - \frac{1}{2} \sum_j \frac{1}{\sigma_j^{(i)2}} \underbrace{\int \mathcal{N}(z; \mu^{(i)}, \sigma^{(i)2}) (z_j-\mu_j^{(i)})^2dz}_{\sigma_j^{(i)2}} \\ = - \frac{J}{2}\log {2\pi} -\frac{1}{2}\sum_j\left(1 + \log \sigma_j^{(i)2}\right)\]

Finally we put this together to compute the KL Divergence term: \(-D_{KL}\left(q_\phi(z|x^{(i)}) || p_\theta(z)\right) = \int q_\phi(z|x)\left(\log p_\theta(z) - \log q_\phi(z|x)\right)dz \\ = \frac{1}{2}\sum_{j=1}^J\left(1 + \log \sigma_j^{(i)2} - \sigma_j^{(i)2} - \mu_j^{(i)2}\right)\)