A kernel framework over parameter space yields confidence bounds for regularized nonlinear models on adaptive data, supporting convergence analysis in Bayesian optimization.
Support vector machines
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GKCM generalizes kernel CI testing to arbitrary regression models, provides uniform asymptotic level guarantees under stated conditions, and outperforms state-of-the-art methods in simulations when using tree-based regressors.
All embedding quantum kernels can be understood as entangled tensor kernels, yielding new insights into their inductive bias and potential dequantization.
A spectral generalized covariance measure enables conditional independence testing on non-Euclidean data with uniform bootstrap validity and power guarantees under doubly robust conditions.
A theoretical framework establishing representer theorems, Sobolev approximation bounds, and spectral convergence for kernel-based learning of spatio-temporal dynamical systems using OV RKHS and Koopman approximations.
citing papers explorer
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Kernel-based guarantees for nonlinear parametric models in Bayesian optimization
A kernel framework over parameter space yields confidence bounds for regularized nonlinear models on adaptive data, supporting convergence analysis in Bayesian optimization.
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The Generalised Kernel Covariance Measure
GKCM generalizes kernel CI testing to arbitrary regression models, provides uniform asymptotic level guarantees under stated conditions, and outperforms state-of-the-art methods in simulations when using tree-based regressors.
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New perspectives on quantum kernels through the lens of entangled tensor kernels
All embedding quantum kernels can be understood as entangled tensor kernels, yielding new insights into their inductive bias and potential dequantization.
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Testing Conditional Independence via the Spectral Generalized Covariance Measure: Beyond Euclidean Data
A spectral generalized covariance measure enables conditional independence testing on non-Euclidean data with uniform bootstrap validity and power guarantees under doubly robust conditions.
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Spatio-Temporal Prediction via Operator-Valued RKHS and Koopman Approximation
A theoretical framework establishing representer theorems, Sobolev approximation bounds, and spectral convergence for kernel-based learning of spatio-temporal dynamical systems using OV RKHS and Koopman approximations.