However, many use cases of neural network verification require solving the inverse problem, or over-approximating the set of inputs that lead to certain outputs.

In this work, we investigate an alternative setting for tuning regularization parameters, namely data-driven algorithm design, following the previous line of work by Balcan et al. [

However, the cross-client edges naturally exist among clients.

In this paper, we study robust algorithms for the experts problem under memory constraints.