Derives a scale-free density bound for the maximum of centered Gaussian vectors with logarithmic dimension dependence that yields uniform control above 2/3 quantiles under a variance separation condition.
Kernel ridge regression inference
2 Pith papers cite this work. Polarity classification is still indexing.
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Sparse Kernels turn kernel ridge regression into end-to-end differentiable PyTorch layers that support training-free transfer, nonlinear probing, and hybrid models while matching or augmenting neural readouts in some settings.
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A scale-free density bound for Gaussian maxima
Derives a scale-free density bound for the maximum of centered Gaussian vectors with logarithmic dimension dependence that yields uniform control above 2/3 quantiles under a variance separation condition.
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Differentiable Kernel Ridge Regression for Deep Learning Pipelines
Sparse Kernels turn kernel ridge regression into end-to-end differentiable PyTorch layers that support training-free transfer, nonlinear probing, and hybrid models while matching or augmenting neural readouts in some settings.