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Found 3 papers, 1 papers with code
Solving Decision-Dependent Games by Learning from Feedback
This paper tackles the problem of solving stochastic optimization problems with a decision-dependent distribution in the setting of stochastic strongly-monotone games and when the distributional dependence is unknown.
Scalable Gaussian Process Hyperparameter Optimization via Coverage Regularization
Gaussian processes (GPs) are Bayesian non-parametric models popular in a variety of applications due to their accuracy and native uncertainty quantification (UQ).
Stochastic Saddle Point Problems with Decision-Dependent Distributions
To find equilibrium points, we develop deterministic and stochastic primal-dual algorithms and demonstrate their convergence with constant step-size in the former and polynomial decay step-size schedule in the latter.
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