Failure Modes of Variational Inference for Decision Making

In this paper we highlight the risks of relying on
mean-field variational inference to learn models
that are used as simulators for decision making.
We study the role of accurate inference for latent
variable models in terms of cumulative reward
performance. We show how naive mean-field
variational inference at test time can lead to poor
decisions in basic but fundamental quadratic control problems with continuous actions, as relevant
correlations in the latent space are ignored. We
then extend these examples to a more complex
non-linear scenario with asymmetric costs, where
regret is even more significant.

• Algorithms and Theory

• Machine Intelligence