Network data commonly consists of observations on a single large network. Accordingly, researchers often partition the network into clusters in order to apply cluster-robust inference methods. All existing such methods require be asymptotically independent. We show that for this requirement hold, under certain conditions, it is necessary and sufficient have small conductance, which ratio edge b...

Since network data commonly consists of observations from a single large network, researchers often partition the into clusters in order to apply cluster?robust inference methods. Existing such methods require be asymptotically independent. Under mild conditions, we prove that, for this requirement hold network?dependent data, it is necessary and sufficient that have low conductance, ratio edge...

Precondition inference is a non-trivial problem with important applications in program analysis and verification. We present novel iterative method for automatically deriving preconditions the safety unsafety of programs. Each iteration maintains over-approximations set safe unsafe initial states; which are used to partition program's states into those known be safe, unknown. then construct rev...