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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9 Pith papers cite this work, alongside 3,933 external citations. Polarity classification is still indexing.
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representative citing papers
An explicit budget allocation condition is derived for two-stage kernel-based operator learning, relating training set size, input observations, and output resolution, alongside a physics-informed online reconstruction extension.
TabKDE generates synthetic tabular data using copula transformations followed by kernel density estimation, matching prior accuracy with negligible training time and reduced storage via coresets.
Several fairness impossibility results share an RKHS geometry where linear mean constraints are overdetermined by unequal base rates, yielding the Pokémon theorem on residual MMD violations and feature-learning collapse.
A quantifier elimination framework for complex numbers is obtained via reduction to real quantifier elimination followed by heuristic reinterpretation, prototyped in the Logic1 system.
Asynchronous sequential updates in KLR Hopfield networks produce statistically indistinguishable trajectories from synchronous dynamics, achieve empirical capacities near P/N=30, and converge with event counts close to initial Hamming distance.
KLR Hopfield networks reach P/N storage of ~16 for random patterns and ~20 for structured data, with limits set by dynamical instability against noise rather than geometric separability per Cover's theorem.
ReTimeCausal is a new EM-based alternating optimization method for causal discovery from irregularly sampled time series that claims consistency guarantees under high missingness.
KLR Hopfield networks exhibit robustness to quantization but sensitivity to pruning, interpreted as arising from dense bimodal parameterization of sparse input mappings.
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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Kernel-based Operator Learning: Error Analysis, Budget Allocation, and a Physics-Informed Extension
An explicit budget allocation condition is derived for two-stage kernel-based operator learning, relating training set size, input observations, and output resolution, alongside a physics-informed online reconstruction extension.
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TabKDE: Simple and Scalable Tabular Data Generation with Kernel Density Estimates
TabKDE generates synthetic tabular data using copula transformations followed by kernel density estimation, matching prior accuracy with negligible training time and reduced storage via coresets.
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The Pok\'emon Theorem and other Fairness Impossibility Results
Several fairness impossibility results share an RKHS geometry where linear mean constraints are overdetermined by unequal base rates, yielding the Pokémon theorem on residual MMD violations and feature-learning collapse.
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Pseudo-Complex Quantifier Elimination
A quantifier elimination framework for complex numbers is obtained via reduction to real quantifier elimination followed by heuristic reinterpretation, prototyped in the Logic1 system.
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Efficient event-driven retrieval in high-capacity kernel Hopfield networks
Asynchronous sequential updates in KLR Hopfield networks produce statistically indistinguishable trajectories from synchronous dynamics, achieve empirical capacities near P/N=30, and converge with event counts close to initial Hamming distance.
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Geometric and dynamical analysis of attractor boundaries and storage limits in kernel Hopfield networks
KLR Hopfield networks reach P/N storage of ~16 for random patterns and ~20 for structured data, with limits set by dynamical instability against noise rather than geometric separability per Cover's theorem.
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Causal Discovery for Irregularly Time Series with Consistency Guarantees
ReTimeCausal is a new EM-based alternating optimization method for causal discovery from irregularly sampled time series that claims consistency guarantees under high missingness.
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Quantization robustness from dense representations of sparse functions in high-capacity kernel associative memory
KLR Hopfield networks exhibit robustness to quantization but sensitivity to pruning, interpreted as arising from dense bimodal parameterization of sparse input mappings.