Arghya Mallick

Learning in Inverse Games: Tractable Training with Probabilistic Guarantees

Inverse game theory seeks to learn agents'unknown objectives from observed equilibrium behavior. Existing residual-based approaches can lead to non-convex problems and need not ensure strong monotonicity of the learned game, limiting reliable equilibrium prediction. We develop a tractable convex framework for learning...