References

David Blei has many good write-ups / talks on this topic

• https://youtu.be/Dv86zdWjJKQ
  • Slides
  • "Review" Paper
• https://www.cs.princeton.edu/courses/archive/fall11/cos597C/lectures/variational-inference-i.pdf

Others

• https://ermongroup.github.io/cs228-notes/inference/variational/
• https://turing.ml/dev/docs/for-developers/variational_inference
• TTIC-31230 - Fundamentals of Deep Learning (ELBO)
• CS 285 (Sergey Levine)

Intro

Assumption:

• x = x_{1:n}: observations
• z = z_{1:m}: hidden variables

We want to model the posterior distribution (notice that this is a form of inference: estimating a hidden variable from observations):

p(z|x) = \frac{p(z, x)}{\int_z p(z, x)}

The posterior links the data and a model.
In most of the interesting problems, calculating denominator is not tractable.
x is the evidence about z.

The main idea: pick a family of distributions over the latent variables parameterized with its own variation parameters:

q(z_{1:m}| \nu)

then find a setting of \nu which makes it closest to the posterior of interest.

The closeness can be measured by Kullback-Leibler (KL) divergence:

\textrm{KL}(q\|p) = E_{Z\sim q} \big[ \log \frac{q(Z)}{p(Z|x)} \big]

We use the arguments (q and p) in this order specifically to take expectation over q. If you flip the order (i.e., \textrm{KL}(p\|q)), that is called expectation propagation. It's a different kind of variational inference and it's more computationally expensive in general.

We cannot minimize this KL divergence directly (why??).
But we can minimize a function that is equal to it up to a constant (ELBO).

The Evidence Lower Bound (ELBO)

\begin{align*} \textrm{KL}\big(q(z)\|p(z|x) \big) &= E_q \bigg[ \log \frac{q(Z)}{p(Z|x)} \bigg] \\ &= E_q [\log q(Z)] - E_q[\log p(Z|x)] \\ &= E_q [\log q(Z)] - E_q[\log p(Z, x) - \log p(x))] \\ &= E_q [\log q(Z)] - E_q[\log p(Z, x)] + \log p(x) \\ &= - (\underline{E_q[\log p(Z, x)] - E_q [\log q(Z)] }) + \log p(x) \\ \end{align*}

Notes:

• The last term \log p(x) is independent of q, thus:
• Minimizing KL divergence is equiv to Maximizing ELBO

The difference between the ELBO and the KL divergence is the log normalizer --- which is what the ELBO bounds (???).

Variational Auto Encoder (Pretty much the same thing)

Latent variable models:

\begin{align*} P_{\Phi, \Theta}(z, x) &= P_\Phi(z) P_\Theta(x|z) \\ P_{\Phi, \Theta}(z | x) &= \frac{P_{\Phi, \Theta}(z, x)}{\int_z P_{\Phi, \Theta}(z, x)} \end{align*}

We have data population, so we want to estimate \Phi and \Theta based on it:

\Phi^*, \Theta^* = \textrm{argmin}_{\Phi, \Theta} E_{x \sim Pop} - log P_{\Phi, \Theta}(x)

(simply cross entropy)

The problem is that we can't typically compute P_{\Phi, \Theta}(x).

• P_{\Phi, \Theta}(x) = \int_z P_\Phi (z) P_\Theta (x|z) dz doesn't work as the sum is too large
• The same sum but with importance sampling with P_{\Phi, \Theta}(z|x) is a better idea but doesn't work: (why???)

Variational Bayes sidesteps this with a model P_\Psi(z|x) that approximate P_{\Phi, \Theta}(z|x).

The ELBO:

\begin{align*} \log P_{\Phi, \Theta}(x) &\geq E_{z \sim P_{\Psi}} \big[ \log P_{\Phi, \Theta}(z, x) \big] - E_{z \sim P_{\Psi}} \big[ \log P_\Psi(z|x) \big] \\ &= E_{z \sim P_{\Psi}} \big[ \log P_{\Theta}(x|z)P_{\Phi}(z) \big] - E_{z \sim P_{\Psi}} \big[ \log P_\Psi(z|x) \big] \\ &= E_{z \sim P_{\Psi}} \big[ - \bigg( \log \frac{P_\Psi(z|x)}{P_{\Phi}(z)} - P_{\Theta}(x|z) \bigg) \big] \end{align*}

Thus,

\Phi^*, \Theta^*, \Psi^* = \textrm{argmin}~E_{x \sim Pop,~z \sim P_\Psi} \big[ \log \frac{P_\Psi(z|x)}{P_{\Phi}(z)} - P_{\Theta}(x|z) \big]

Minor but important: we can't do gradient descent w.r.t. \Psi as there's sampling procedure. We use re-parameterization trick to circumvent this.

• P_\Phi(z): the prior
• P_\Psi(z|x): the encoder
• P_\Theta(x|z): the decoder
• E[\log P_\Psi(z|x)/P_\Phi(z)]: rate term, KL-divergence