Algorithmic stability is a major theme in learning theory, where seminal results have firmly established its close relationship with generalization. Recent research has further highlighted the intricate interplay between stability and additional properties of interest beyond statistical generalization.

It is desirable to restrict the number of components (i.e., encourage sparsity) for

In this work, we study the low-rank MDPs with adversarially changed losses in the full-information feedback setting.