The paper adapts forward and reverse gradient techniques from hyperparameter optimization to solve continuous high-dimensional Stackelberg games, with complexity trade-offs and an adversarial regression application.
In: Proceedings of the 17th ACM SIGKDD international c onfer- ence on Knowledge discovery and data mining
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Gradient Methods for Solving Stackelberg Games
The paper adapts forward and reverse gradient techniques from hyperparameter optimization to solve continuous high-dimensional Stackelberg games, with complexity trade-offs and an adversarial regression application.