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The paper introduces the Clustering Occupation Kernel (CLOCK) method, a representation-based framework that embeds time series from dynamical systems into an RKHS to enable clustering via maximum mean discrepancy between attractors, validated on synthetic data and intracranial EEG recordings.
This paper presents a local and sparse kernel method for learning solution operators of incompressible flows, using divergence-free local interpolants and compactly supported kernels with Operator Lagrange weights to achieve sparse, GPU-parallel regression that outperforms neural operators on ten fluid benchmarks.
该论文提出了一种基于几何的有限特征联想记忆容量理论，将检索干扰分解为有限特征噪声与结构性干扰，并推导出与表示几何直接挂钩的容量上限预测，随后在合成、视觉和医学图像表示上进行了验证。
This CIKM 2026 paper characterizes "convergence collapse" in fixed-target tabular anomaly detectors, where better optimization removes anomaly signal, and proposes Kernel Contraction Matching (KCM), a closed-form kernel-regression detector, plus the Kernel-Anchored Regularizer (KAR) to mitigate collapse and improve AUROC on ADBench.
FlashDiffusion is a matrix-free method that evaluates dense Gaussian kernel blocks in fused GPU tiles and couples the eigensolver to an empirical beta-flow that selects the finite-sample resolution scale, avoiding O(N^2) memory costs in diffusion-map spectral decompositions.
KernelOnet proposes an interpretable neural operator framework that explicitly incorporates kernel functions in place of trunk networks, offering data-driven, physics-informed, and hybrid kernel strategies for PDE solving with higher accuracy and fewer parameters than DeepONet.
This paper develops two resource-efficient distributed recursive Gaussian process algorithms for multi-output regression in multi-agent systems, reducing communication overhead while maintaining estimation accuracy.
This paper proposes an adaptive spectral bandwidth control method for kernelized graph construction to align kernel spectral properties with intrinsic manifold dimensions, showing improvements in self-supervised learning embedding tasks on CIFAR-100.
This paper develops NTK-KIP, MetaQuill, and MetaQuill-KIP algorithms to improve neural field reconstruction from sparse observations, making NTK-driven neural fields non-linear and meta-learnable for efficient few-shot adaptation.
This paper presents a method combining neural network means with exact Matérn kernel corrections for operator learning in PDEs, achieving competitive or improved performance on public benchmarks like structural mechanics and OCO-2 radiative transfer emulation.
This paper introduces General Coded Computing (GCC), a learning-theoretic framework for mitigating stragglers in distributed computing systems, providing theoretical performance guarantees and experimental validation on deep neural networks.
The paper presents the Frame Kernel Method, a novel multiscale operator learning approach for surrogate modeling of PDEs that uses kernel frame approximations and achieves higher accuracy than popular neural operators while enabling multiscale decomposition.
This paper introduces a deterministic hyperparameter selection method for reservoir computing using free-probability kernels, which eliminates the need for resource-intensive rollouts and achieves performance similar to exhaustive search with significantly lower cost.
This paper proposes a joint-distribution approach to fair representation learning with continuous sensitive attributes, using HSIC as a joint discrepancy to avoid conditional density estimation. It proves statistical efficiency gains over conditional-route methods and introduces the FRHSIC algorithm with comparable fairness-accuracy tradeoffs and faster training.
The paper introduces NysHD, a method that bridges hyperdimensional computing and kernel methods via the Nyström approximation, allowing any positive-semidefinite similarity function to be used as an HDC encoding. It demonstrates improved classification accuracy on graph and string datasets compared to existing HDC encoding methods.
This paper introduces K-Inverse-RFM, a modified Recursive Feature Machine that bridges the performance gap between RFMs and neural networks on mathematical tasks corrupted by label noise, imbalanced data, or alternative data representations.
Introduces Weak-form Kernel Ridge Regression (WKRR) for learning dynamical systems from noisy measurements, combining a weak formulation with kernel ridge regression to filter noise and improve accuracy. The method outperforms baseline methods on chaotic benchmarks up to 64 dimensions and 15,000-dimensional real-world fluid data.
This paper introduces Bernstein–Schur kernels, a class of nonstationary kernels between shift-invariant and dot-product templates, and provides a random feature construction by sketching the finite modulation and randomizing the completely monotone radial factor. The method yields unbiased estimators with operator-norm bounds controlled by intrinsic dimensions, and experiments validate the approach on a biased kernel example.
This paper identifies the geometric mechanism behind boundary-induced acquisition bias in Gaussian processes on bounded domains, showing how kernel truncation inflates posterior variance and distorts acquisition functions independently of the objective function. The authors introduce a function-free diagnostic to quantify this bias across different acquisition classes.
TorchKM is an open-source GPU-accelerated library for kernel machines (SVMs, kernel logistic regression, etc.) with a scikit-learn-style API. It accelerates training and model selection by reusing matrix operations, offering substantial speedups over standard baselines.