Trade-off functions between two distributions are finitely testable if and only if their Neyman-Pearson rejection regions are attainable by a VC-class of sets.
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An augmented kernel ridge regression estimator separates linear and nonlinear components to achieve sharp oracle inequalities and minimax optimal prediction risk under general kernels.
PAC learning-based DR-MPC framework interpolates between robust MPC and stochastic MPC for interactive trajectory planning under agent decision uncertainty.
citing papers explorer
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When Are Trade-Off Functions Testable from Finite Samples?
Trade-off functions between two distributions are finitely testable if and only if their Neyman-Pearson rejection regions are attainable by a VC-class of sets.
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Adaptive Kernel Ridge Regression with Linear Structure: Sharp Oracle Inequalities and Minimax Optimality
An augmented kernel ridge regression estimator separates linear and nonlinear components to achieve sharp oracle inequalities and minimax optimal prediction risk under general kernels.
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Interactive Trajectory Planning with Learning-based Distributionally Robust Model Predictive Control and Markov Systems
PAC learning-based DR-MPC framework interpolates between robust MPC and stochastic MPC for interactive trajectory planning under agent decision uncertainty.