Limitations of the Empirical Fisher Approximation for Natural Gradient DescentLimitations of the Empirical Fisher Approximation for Natural Gradient DescentFrederik Kunstner and Lukas Balles and Philipp Hennig2019
Paper summaryrobertmuellerThe authors analyse in the very well written paper the relation between Fisher $F(\theta) = \sum_n \mathbb{E}_{p_{\theta}(y \vert x)}[\nabla_{\theta} \log(p_{\theta}(y \vert x_n))\nabla_{\theta} \log(p_{\theta}(y \vert x_n))^T] $ and empirical Fisher $\bar{F}(\theta) = \sum_n [\nabla_{\theta} \log(p_{\theta}(y_n \vert x_n))\nabla_{\theta} \log(p_{\theta}(y_n \vert x_n))^T] $, which has recently seen a surge in interest. . The definitions differ in that $y_n$ is a training label instead of a sample of the model $p_{\theta}(y \vert x_n)$, thus even so the name suggests otherwise $\bar{F}$ is not a empirical, for example Monte Carlo, estimate of the Fisher. The authors rebuff common arguments used to justify the use of the empirical fisher by an amendment to the generalized Gauss-Newton, give conditions when the empirical Fisher does indeed approach the Fisher and give an argument why the empirical fisher might work in practice nonetheless.
The Fisher, capturing the curvature of the parameter space, provides information about the geometry of the parameters pace, the empirical Fisher might however fail so capture the curvature as the striking plot from the paper shows:
https://i.imgur.com/c5iCqXW.png
The authors rebuff the two major justifications for the use of empirical Fisher:
1. "the empirical Fisher matches the construction of a generalized Gauss-Newton"
* for the log-likelihood $log(p(y \vert f) = \log \exp(-\frac{1}{2}(y-f)^2))$ the generalized Gauss-Newton intuition that small residuals $f(x_n, \theta) - y_n$ lead to a good approximation of the Hessian is not satisfied. Whereas the Fisher approaches the Hessian, the empirical Fisher approaches 0
2. "the empirical Fisher converges to the true Fisher when the model is a good fit for the data"
* the authors sharpen the argument to "the empirical Fisher converges at the minimum to the Fisher as the number of samples grows", which is unlikely to be satisfied in practice.
The authors provide an alternative perspective on why the empirical Fisher might be successful, namely to adapt the gradient to the gradient noise in stochastic optimization. The empirical Fisher coincides with the second moment of the stochastic gradient estimate and encodes as such covariance information about the gradient noise. This allows to reduce the effects of gradient noise by scaling back the updates in high variance aka noise directions.
First published: 2019/05/29 (1 year ago) Abstract: Natural gradient descent, which preconditions a gradient descent update with
the Fisher information matrix of the underlying statistical model, is a way to
capture partial second-order information. Several highly visible works have
advocated an approximation known as the empirical Fisher, drawing connections
between approximate second-order methods and heuristics like Adam. We dispute
this argument by showing that the empirical Fisher---unlike the Fisher---does
not generally capture second-order information. We further argue that the
conditions under which the empirical Fisher approaches the Fisher (and the
Hessian) are unlikely to be met in practice, and that, even on simple
optimization problems, the pathologies of the empirical Fisher can have
undesirable effects.
The authors analyse in the very well written paper the relation between Fisher $F(\theta) = \sum_n \mathbb{E}_{p_{\theta}(y \vert x)}[\nabla_{\theta} \log(p_{\theta}(y \vert x_n))\nabla_{\theta} \log(p_{\theta}(y \vert x_n))^T] $ and empirical Fisher $\bar{F}(\theta) = \sum_n [\nabla_{\theta} \log(p_{\theta}(y_n \vert x_n))\nabla_{\theta} \log(p_{\theta}(y_n \vert x_n))^T] $, which has recently seen a surge in interest. . The definitions differ in that $y_n$ is a training label instead of a sample of the model $p_{\theta}(y \vert x_n)$, thus even so the name suggests otherwise $\bar{F}$ is not a empirical, for example Monte Carlo, estimate of the Fisher. The authors rebuff common arguments used to justify the use of the empirical fisher by an amendment to the generalized Gauss-Newton, give conditions when the empirical Fisher does indeed approach the Fisher and give an argument why the empirical fisher might work in practice nonetheless.
The Fisher, capturing the curvature of the parameter space, provides information about the geometry of the parameters pace, the empirical Fisher might however fail so capture the curvature as the striking plot from the paper shows:
https://i.imgur.com/c5iCqXW.png
The authors rebuff the two major justifications for the use of empirical Fisher:
1. "the empirical Fisher matches the construction of a generalized Gauss-Newton"
* for the log-likelihood $log(p(y \vert f) = \log \exp(-\frac{1}{2}(y-f)^2))$ the generalized Gauss-Newton intuition that small residuals $f(x_n, \theta) - y_n$ lead to a good approximation of the Hessian is not satisfied. Whereas the Fisher approaches the Hessian, the empirical Fisher approaches 0
2. "the empirical Fisher converges to the true Fisher when the model is a good fit for the data"
* the authors sharpen the argument to "the empirical Fisher converges at the minimum to the Fisher as the number of samples grows", which is unlikely to be satisfied in practice.
The authors provide an alternative perspective on why the empirical Fisher might be successful, namely to adapt the gradient to the gradient noise in stochastic optimization. The empirical Fisher coincides with the second moment of the stochastic gradient estimate and encodes as such covariance information about the gradient noise. This allows to reduce the effects of gradient noise by scaling back the updates in high variance aka noise directions.