We show for the first time that this leads both to improved convergence bounds under standard assumptions.

We propose estimating Gaussian graphical models (GGMs) that are fair with respect to sensitive nodal attributes .

Across a wide range of hardware scenarios, the computational efficiency and physical size of the arithmetic units significantly influence the speed and footprint of the overall hardware system.

We perform experiments on high-dimensional Gaussians, Lotka-V olterra dynamics, and a Pickover attractor.

As such, we prevent policies from merely exploiting heuristic rewards without improving the task reward.

Our results are corroborated with experiments.