  We tackle here a specific, still not widely addressed aspect, of AI
robustness, which consists of seeking invariance / insensitivity of model
performance to hidden factors of variations in the data. Towards this end, we
employ a two step strategy that a) does unsupervised discovery, via generative
models, of sensitive factors that cause models to under-perform, and b)
intervenes models to make their performance invariant to these sensitive
factors' influence. We consider 3 separate interventions for robustness,
including: data augmentation, semantic consistency, and adversarial alignment.
We evaluate our method using metrics that measure trade offs between invariance
(insensitivity) and overall performance (utility) and show the benefits of our
method for 3 settings (unsupervised, semi-supervised and generalization).
