Toourknowledge, ADASPIDER isthefirstparameterfree non-convex variance-reduction method in the sense that it does not require the knowledge of problem-dependent parameters, such as smoothness constant L,targetaccuracyϵoranybound ongradient norms.

Much of the work in online learning focuses on the study of sublinear upper bounds on the regret.

In the natural sciences, physics has found great success by describing the universe in terms of symmetry preserving transformations. Inspired by this formalism, we propose a framework, built upon the theory of group representation, for learning representations of a dynamical environment structured around the transformations that generate its evolution. Experimentally, we learn the structure of explicitly symmetric environments without supervision from observational data generated by sequential interactions.