Finite-dimensional RKHS approximation via n-widths enables scenario optimization to deliver violation guarantees on nonlinear one-step predictors without a priori bounds on the true RKHS norm or Lipschitz constant.
Wait-and-Judge Scenario Optimization
4 Pith papers cite this work. Polarity classification is still indexing.
representative citing papers
Explicit formulas are proven for the depth functions of powers of cover ideals of path graphs.
Reinforcement learning learns a policy that adapts control parameters of a regularized interior-point method, accelerating high-accuracy solutions for convex quadratic programs and generalizing across problem classes after lightweight training.
Low-latency analytical and numerical quasi-static models for UAV tether aerodynamics are proposed and validated with load cell tests.
citing papers explorer
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Robust Nonlinear System Identification in Reproducing Kernel Hilbert Spaces via Scenario Optimization
Finite-dimensional RKHS approximation via n-widths enables scenario optimization to deliver violation guarantees on nonlinear one-step predictors without a priori bounds on the true RKHS norm or Lipschitz constant.
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The depth function of powers of cover ideals of path graphs
Explicit formulas are proven for the depth functions of powers of cover ideals of path graphs.
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Reinforcement learning for adaptive interior point methods in convex quadratic programming
Reinforcement learning learns a policy that adapts control parameters of a regularized interior-point method, accelerating high-accuracy solutions for convex quadratic programs and generalizing across problem classes after lightweight training.
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Low-Latency Quasi-Static Modeling of UAV Tether Aerodynamics
Low-latency analytical and numerical quasi-static models for UAV tether aerodynamics are proposed and validated with load cell tests.