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.
4
Pith papers citing it
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
-
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.
-
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.
-
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.
-
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.