  A wide array of modern machine learning applications - from adversarial
models to multi-agent reinforcement learning - can be formulated as
non-cooperative games whose Nash equilibria represent the system's desired
operational states. Despite having a highly non-convex loss landscape, many
cases of interest possess a latent convex structure that could potentially be
leveraged to yield convergence to equilibrium. Driven by this observation, our
paper proposes a flexible first-order method that successfully exploits such
"hidden structures" and achieves convergence under minimal assumptions for the
transformation connecting the players' control variables to the game's latent,
convex-structured layer. The proposed method - which we call preconditioned
hidden gradient descent (PHGD) - hinges on a judiciously chosen gradient
preconditioning scheme related to natural gradient methods. Importantly, we
make no separability assumptions for the game's hidden structure, and we
provide explicit convergence rate guarantees for both deterministic and
stochastic environments.
