For fixed retention probability θ, the process log L_⌊nt⌋ obeys a functional LDP with geometric-mark entropy rate, an MDP with the CLT Gaussian RKHS rate, and a Strassen LIL with that unit ball as cluster set.
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8 Pith papers cite this work, alongside 5,435 external citations. Polarity classification is still indexing.
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Heat-kernel smoothing over weighted points on a compact manifold yields a scale-dependent geometric effective sample size that discounts nearby and duplicate particles.
KAHM yields a compute-efficient query encoder that outperforms matched learned adapters in reconstructing a frozen Mixedbread embedding space on an Austrian-law retrieval task while delivering an 8.53x CPU speedup.
Proves that rescaled deviations of kernel gradient flow and infinitesimal gradient boosting from their deterministic ODE limits converge to a Gaussian process via a general stochastic perturbation analysis of ODEs in Banach spaces.
Identifies some discrete Wallach sets for weighted H-harmonic Bergman spaces and shows structure depends on dimension parity.
A kernel-based regularized learning framework for FDR control that unifies arbitrary structures and supplies provably valid decision rules with likelihood-based tuning.
The paper introduces 'Platonic Projection Structures,' a reformulation of standard PSD operator theory applied to representation learning, with experiments that verify definitions rather than test predictions.
citing papers explorer
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Functional Limit Theorems for Random Least Common Multiples
For fixed retention probability θ, the process log L_⌊nt⌋ obeys a functional LDP with geometric-mark entropy rate, an MDP with the CLT Gaussian RKHS rate, and a Strassen LIL with that unit ball as cluster set.
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Heat-Kernel Entropy Profiles and Geometric Effective Sample Size for Weighted Measures on Manifolds
Heat-kernel smoothing over weighted points on a compact manifold yields a scale-dependent geometric effective sample size that discounts nearby and duplicate particles.
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Kernel Affine Hull Machines as Compute-Efficient Encoders for Frozen Semantic Spaces
KAHM yields a compute-efficient query encoder that outperforms matched learned adapters in reconstructing a frozen Mixedbread embedding space on an Austrian-law retrieval task while delivering an 8.53x CPU speedup.
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A functional central limit theorem for kernel gradient flow and infinitesimal gradient boosting
Proves that rescaled deviations of kernel gradient flow and infinitesimal gradient boosting from their deterministic ODE limits converge to a Gaussian process via a general stochastic perturbation analysis of ODEs in Banach spaces.
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Analytic continuation of weighted $H$-harmonic Bergman spaces
Identifies some discrete Wallach sets for weighted H-harmonic Bergman spaces and shows structure depends on dimension parity.
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Controlling False Discovery in Arbitrarily Structured Hypothesis Spaces via Reproducing Kernels
A kernel-based regularized learning framework for FDR control that unifies arbitrary structures and supplies provably valid decision rules with likelihood-based tuning.
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Platonic Projection Structures: Operator-Induced Observability in Representation Learning
The paper introduces 'Platonic Projection Structures,' a reformulation of standard PSD operator theory applied to representation learning, with experiments that verify definitions rather than test predictions.
- On the sharp linear convergence rate of the circumcentered--reflection method on subspaces