Implicit bias of SGD in $L_2$-regularized linear DNNs: One-way jumps from high to low rank
Authors: Zihan Wang,Arthur Jacot
Presentation: ICLR 2024,Spotlight

Abstract: The $L_{2}$-regularized loss of Deep Linear Networks (DLNs) with
more than one hidden layers has multiple local minima, corresponding
to matrices with different ranks. In tasks such as matrix completion,
the goal is to converge to the local minimum with the smallest rank
that still fits the training data. While rank-underestimating minima
can be avoided since they do not fit the data, GD might get
stuck at rank-overestimating minima. We show that with SGD, there is always a probability to jump
from a higher rank minimum to a lower rank one, but the probability
of jumping back is zero. More precisely, we define a sequence of sets
$B_{1}\subset B_{2}\subset\cdots\subset B_{R}$ so that $B_{r}$
contains all minima of rank $r$ or less (and not more) that are absorbing
for small enough ridge parameters $\lambda$ and learning rates $\eta$:
SGD has prob. 0 of leaving $B_{r}$, and from any starting point there
is a non-zero prob. for SGD to go in $B_{r}$.