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I've been learning about normalizing flow. This is my understanding, and please correct me whenever I am wrong.

There are $\{y_1,y_2,...,y_n\}$ samples from an unknown distribution $p_y(y)$ that we wish to learn. We can consider a distribution that is well-known such as Gaussian with density $p_y(x)$. Then, the goal of normalizing flow is to find a $f_{\theta}$ with parameters $\theta$ such that $Y=f_{\theta}(X)$. To find $p_y(y)$ or $\log (p_y(y))$ we need to employ the change of variable formula, namely: $$ \log(p_y(y))=\log(p_x(f_{\theta}^{-1}(y)))-\log\left|\dfrac{dg}{dx} \right |=\log(p_x(x))-\log\left|\dfrac{df_{\theta}}{dx} \right | $$ I know that maximum likelihood estimation is used to find $\theta$. However, I am not sure exactly how that works. I'd appreciate if someone could show me some details on that. The examples I've seen online usually just use a python library and details are not included.

*Edit: my question was: how are the data points $\{y_1,y_2,...,y_n\}$ exactly used to the gradient ascent on the likelihood function?

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  • $\begingroup$ Which details are confusing to you? We can give more helpful answers if you tell us where particularly you're stuck. $\endgroup$
    – Arya McCarthy
    Apr 10, 2021 at 22:31
  • $\begingroup$ I don't know how the maximum likelihood estimation is executed. In particular, I would not know how use $\{y_1,y_2,...,y_n\}$ and $\{x_1,x_2,...,x_n\}$ when doing the estimation. $\endgroup$
    – Schach21
    Apr 10, 2021 at 22:42
  • $\begingroup$ Do you understand how MLE works when there isn't a normalizing flow? $\endgroup$
    – Arya McCarthy
    Apr 10, 2021 at 22:46
  • $\begingroup$ yes, I am familiar with MLE, just not with normalizing flow. $\endgroup$
    – Schach21
    Apr 10, 2021 at 22:53

1 Answer 1

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Overview.
You're familiar with MLE, which is a good starting point. We have a parametric model whose parameters $\theta$ we seek to optimize, in order to maximize the likelihood of our model $L(\theta \mid \{y_1,y_2,...,y_n\}) = p_Y(\{y_1,y_2,...,y_n\} \mid \theta)$. Gradient-based methods are one way to do the optimization.

It's no different with normalizing flows: we're trying to maximize the likelihood. The transformation $f$ has some parameters $\theta$ that we seek to optimize, possibly with gradient ascent. (The prior distribution on $x$, $p_X$, may have its own parameters $\phi$ to learn. Or we could just declare it to be a standard Gaussian with the same dimension as $Y$ or something. This means that only $f$ has learnable parameters.) These parameters are just like any other.

About $f$.
The transformation $f$ must have three important properties that we will use in a moment. It is deterministic; it is invertible; and it has an easily computable, easily differentiable determinant of the Jacobian. Some example functions satisfying these requirements are here.

Computing the likelihood.
Mechanically, how would you compute $p_Y(y_i \mid \theta)$? Remember that $p_Y(y_i \mid \theta) = p_X\left(f^{-1}_\theta(y_i)\right) \cdot \left|\det \left(\frac{\mathrm{d}f^{-1}_\theta}{\mathrm{d}x}\right)\right|$. (Your question was missing the $\det$.)

  1. Apply the inverse transform of your observed variable $y_i$ to compute the latent variable $x_i = f^{-1}_\theta(y_i)$.
  2. Compute the probability density $p_X(x_i)$.
  3. Compute the $\left|\det \left(\frac{\mathrm{d}f^{-1}_\theta}{\mathrm{d}x}\right)\right|$ term. This is easy because of the functional form we chose.
  4. Multiply these terms to get the likelihood.

Learning $\theta$.
To actually learn $\theta$ with gradient descent requires finding and following the gradient. I'll suggest a way assuming you're working in a computing toolkit that supports reverse-mode automatic differentiation (back-propagation). There are other options as well; heck, you could define the gradients by hand if you hate yourself want to.

  1. Backpropagate from the negative log of the likelihood to compute gradients with respect to $\theta$.
  2. Adjust $\theta$ by taking a step in the direction down the gradient.
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  • $\begingroup$ So, in terms of computation, there's never a need to sample a Gaussian (assuming we are using Gaussian), namely get $\{x_1,x_2,...,x_n\}$ directly from the Gaussian? Also, thanks for your answer! it's very helpful! $\endgroup$
    – Schach21
    Apr 11, 2021 at 0:29
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    $\begingroup$ Right—you never need to sample from $p_X$ if it's simple to evaluate its pdf. If the distribution is more complicated, perhaps you'll need a Monte Carlo estimate of the pdf. $\endgroup$
    – Arya McCarthy
    Apr 11, 2021 at 2:31

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