  There often is a dilemma between ease of optimization and robust
out-of-distribution (OoD) generalization. For instance, many OoD methods rely
on penalty terms whose optimization is challenging. They are either too strong
to optimize reliably or too weak to achieve their goals.
  We propose to initialize the networks with a rich representation containing a
palette of potentially useful features, ready to be used by even simple models.
On the one hand, a rich representation provides a good initialization for the
optimizer. On the other hand, it also provides an inductive bias that helps OoD
generalization. Such a representation is constructed with the Rich Feature
Construction (RFC) algorithm, also called the Bonsai algorithm, which consists
of a succession of training episodes. During discovery episodes, we craft a
multi-objective optimization criterion and its associated datasets in a manner
that prevents the network from using the features constructed in the previous
iterations. During synthesis episodes, we use knowledge distillation to force
the network to simultaneously represent all the previously discovered features.
  Initializing the networks with Bonsai representations consistently helps six
OoD methods achieve top performance on ColoredMNIST benchmark. The same
technique substantially outperforms comparable results on the Wilds Camelyon17
task, eliminates the high result variance that plagues other methods, and makes
hyperparameter tuning and model selection more reliable.
