Kernel Method

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Period ending 2026-09-21

7 new papers

A weekly snapshot of new work published in Kernel Method.

Period ending 2026-09-14

2 new papers

A weekly snapshot of new work published in Kernel Method.

149 papers

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Frequency-Scale Saliency for Spectral Descriptor Analysis in 3D Shape Retrieval
Jun 5, 2026Jianru ShenMultiscale Kernel Method
TorchKM: A GPU-Oriented Library for Kernel Learning and Model Selection
Jun 4, 2026Yikai Zhang, Gaoxiang Jia, Jie Ding +1Graphics Processing Unit Kernels Pytorch
Generating Financial Time Series by Matching Random Convolutional Features
Jun 3, 2026Konrad J. Mueller, Nikita Zozoulenko, Ben Wood +2Synthetic Time Series Time Series
KODA: Contrastive Representation Comparison and Alignment for Vision-Language Foundation Models
Jun 2, 2026Youqi Wu, Mohammad Jalali, Farzan FarniaVision-Language Contrastive
A Doeblin-Anchored Contrastive Chart for Learning Markov Transition Kernels
Jun 1, 2026Ao XuMarkov Transitions
Blurry Window Attention
May 31, 2026Axel Laborieux, Christos Sourmpis, Juan Gabriel Kostelec +1Softmax Attention Kernel Method
AdaKernel: Learning Adaptive Kernel Parameters for Spatiotemporal Graph Neural Networks
May 31, 2026Zhongyue Zhang, Guangyin Jin, Yuxuan Liang +2Graph Neural Networks Kernel Method
On Finite-sample Concentration of Median of Incomplete U-Statistics
May 30, 2026Nong Minh Hieu, Antoine LedentGeneralized U-Statistics Estimators
Eigen-Spike Emergence and Quadratic Equivalents for Conjugate Kernels on Nonlinearly Separable Data
May 28, 2026Collin Cranston, Zhichao Wang, Todd Kemp +1Kernel Method Separability
Probabilistic Smoothing with Ratio-Monotone Transforms for Global Optimization
May 26, 2026Kukyoung Jang, Taehyun Cho, Junrui Zhang +2Smoothing Kernel Method
Nonlinear Data Integration via Kernel Methods for Data Collaboration Analysis
May 26, 2026Yamato Suetake, Yuta Kawakami, Shunnosuke Ikeda +1Kernel Method Dimensionality Reduction
SPHERE-JEPA: Spherical Prediction with Homogeneous Embeddings
May 26, 2026Léo Nicollier, Max Dunitz, Marc Pic +3Manifold Learning Method Embedding Dimension
SIKA-GP: Accelerating Gaussian Process Inference with Sparse Inducing Kernel Approximations for Bayesian Deep Learning
May 26, 2026Wenyuan Zhao, Rui Tuo, Chao TianGaussian Process Multi-Gpu Systems
Conditional KRR: Injecting Unpenalized Features into Kernel Methods with Applications to Kernel Thresholding
May 25, 2026Rustem Takhanov, Zhenisbek AssylbekovKernel Ridge Regression Kernel Method
Branched Signature Kernel Solvers for ODEs with rough Single-Trajectory signals
May 25, 2026Munawar Ali, Qi Feng, Charlie Pyle +1Ordinary Differential Equations Kernel Method
Learning View-Dependent Splatting Kernels
May 25, 2026Huakeng Ding, Zhanpeng Liu, Fan Pei +2Novel View Synthesis Gaussian Splatting
Nyström Kernel Stein Discrepancy Tests
May 24, 2026Florian Kalinke, Zoltán Szabó, Bharath K. SriperumbudurStatistical Models Kernel Method
LAPLEX: The FFT of Learnable Laplace Kernels
May 23, 2026Łukasz Struski, Hanna Blazhko, Piotr Kubaty +1Matrix Multiplication Kernel Method
Distance-Aware Joint Spatio-Temporal Graph Contrastive Learning for Major Depressive Disorder Diagnosis
May 22, 2026Muhammad Asif Hasan, Yanming Zhu, Xuefei Yin +1Functional Networks Contrastive Learning
Adaptive Measurement Allocation for Learning Kernelized SVMs Under Noisy Observations
May 21, 2026Artur MiroszewskiQuantum Kernels Support Vector Machines
IKNO: Infinite-order Kernel Neural Operators
May 21, 2026Pengyuan Zhu, Ivor W. Tsang, Yueming LyuNeural Operators Fourier Neural Operators
Exact Uniform L1 Spacing for Solow-Polasky Diversity on Lines and Ordered Pareto Fronts
May 21, 2026Michael T. M. Emmerich, Mahboubeh Nezhadmoghaddam, Jesús Guillermo Falcón CardonaPareto Frontier Maximum Independent Set
Adaptive RBF-KAN: A Comparative Evaluation of Dynamic Shape Parameters in Kolmogorov-Arnold Networks
May 20, 2026Roberto Cavoretto, Alessandra De Rossi, Adeeba Haider +1Kolmogorov-Arnold Networks Radial Basis Function
3D Skew Gaussian Splatting with Any Camera Trajectory Visualization Engine
May 18, 2026Beizhen Zhao, Yifan Zhou, Gaochao Song +23D Gaussian Rasterization
Automated Kernel Discovery Towards Understanding High-dimensional Bayesian Optimization
May 18, 2026Taeyoung Yun, Woocheol Shin, Inhyuck Song +2Bayesian Optimization Gaussian Process
How does feature learning reshape the function space?
May 18, 2026João Lobo, Bruno Loureiro, Long Tran-Than +1Feature Learning Kernel Method
TabKDE: Simple and Scalable Tabular Data Generation with Kernel Density Estimates
May 17, 2026Meysam Alishahi, Yan Zheng, Junpeng Wang +2Synthetic Tabular Data Gaussian Copula
CompactAttention: Accelerating Chunked Prefill with Block-Union KV Selection
May 16, 2026Jiwon Song, Dongwon Jo, Beomseok Kang +1Prefill Efficient Long-Context Inference
Average Gradient Outer Product in kernel regression provably recovers the central subspace for multi-index models
May 14, 2026Libin Zhu, Damek Davis, Dmitriy Drusvyatskiy +1Kernel Ridge Regression Kernel Method
Large Dimensional Kernel Ridge Regression: Extending to Product Kernels
May 14, 2026Yang Zhou, Yicheng Li, Yuqian Cheng +1Kernel Ridge Regression Kernel Method
Randomized Atomic Feature Models for Physics-Informed Identification of Dynamic Systems
May 14, 2026Rajiv Singh, Mario Sznaier, Lennart LjungSystem Identification Physics-Informed Machine Learning
Support-Conditioned Flow Matching Is Kernel Smoothing
May 13, 2026Daniel Matsui SmolaKernel Method Conditional Flow Matching
Chem-GMNet: A Sphere-Native Geometric Transformer for Molecular Property Prediction
May 13, 2026Deepak Warrier, Raja Sekhar PappalaMolecular Structure Elucidation Molecular Property Prediction
State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives
May 13, 2026Hoang-Son Tran, Pranav Gupta, Rémi Bardenet +1Mini-Batch Stochastic Gradient Descent Subsampling
Sobolev Regularized MMD Gradient Flow
May 12, 2026Chenyang Tian, Bharath K. Sriperumbudur, Arthur Gretton +1Maximum Mean Discrepancy Wasserstein Gradient Flows
Stop Marginalizing My Dreams: Model Inversion via Laplace Kernel for Continual Learning
May 12, 2026Patryk Krukowski, Jacek Tabor, Przemysław Spurek +2Efficient Continual Learning Continual Learning Methods
Interpretable Machine Learning for Spatial Science: A Lie-Algebraic Kernel for Rotationally Anisotropic Gaussian Processes
May 11, 2026Kane Warrior, Dalia ChakrabartyGaussian Process Anisotropy
Kernel-Gradient Drifting Models
May 11, 2026Maria Esteban-Casadevall, Jorge Carrasco-Pollo, Max Welling +3Wasserstein Gradient Flows Generative Models
Multifidelity Gaussian process regression for solving nonlinear partial differential equations
May 11, 2026Fatima-Zahrae El-Boukkouri, Josselin Garnier, Olivier RoustantGaussian Process Classical Numerical Solver
Scalable Gaussian process inference via neural feature maps
May 11, 2026Anthony StephensonGaussian Process Kernel Method
A Quantum Inspired Variational Kernel and Explainable AI Framework for Cross Region Solar and Wind Energy Forecasting
May 9, 2026Pavan Manjunath, Thomas PruferEnergy Forecasting Solar
Don't Get Your Kroneckers in a Twist: Gaussian Processes on High-Dimensional Incomplete Grids
May 8, 2026Mads Greisen Højlund, August Smart Lykke-Møller, Henry Moss +1Gaussian Process Computational Chemistry
Semiparametric Efficient Test for Interpretable Distributional Treatment Effects
May 8, 2026Houssam Zenati, Arthur GrettonHeterogeneous Treatment Effects Two-Sample Testing
Kernel Selection is Model Selection: A Unified Complexity-Penalized Approach for MMD Two-Sample Tests
May 7, 2026Yijin Ni, Xiaoming HuoMaximum Mean Discrepancy Kernel Method
Gaussian mixture models in Hilbert spaces via kernel methods
May 7, 2026Daniel López-Montero, Antonio Álvarez-López, Marcos MatabuenaKernel Hilbert Spaces Gaussian Mixture Models
Sharper Guarantees for Misspecified Kernelized Bandit Optimization
May 7, 2026Davide Maran, Csaba SzepesváriKernel Method Bandits
QuadraSHAP: Stable and Scalable Shapley Values for Product Games via Gauss-Legendre Quadrature
May 7, 2026Majid Mohammadi, Grigory Reznikov, Pavel Sinitcyn +2Shapley Value Coalition Formation
A Closed-Form Adaptive-Landmark Kernel for Certified Point-Cloud and Graph Classification
May 5, 2026Sushovan Majhi, Atish Mitra, Žiga Virk +1Kernel Method Certified Point-Cloud
Differentiable Kernel Ridge Regression for Deep Learning Pipelines
May 4, 2026Jean-Marc Mercier, Gabriele SantinKernel Method Kernel Ridge Regression
Complex Diffusion Maps with $ω$ -Parameterized Kernels Revealing Inherent Harmonic Representations
May 3, 2026Tongzhen Dang, Weiyang Ding, Michael K. NgKernel Method Invertible Flows
New Bounds for Kernel Sums via Fast Spherical Embeddings
May 2, 2026Tal WagnerKernel Method Optimal Sample Complexity
SHIFT: Robust Double Machine Learning for Average Dose-Response Functions under Heavy-Tailed Contamination
Apr 30, 2026Eichi UeharaContamination-Aware Benchmarking Framework Outliers
Robust Learning on Heterogeneous Graphs with Heterophily: A Graph Structure Learning Approach
Apr 30, 2026Yihan Zhang, Ercan E. KuruogluGraph Representation Learning Text-Attributed Graph
Laplace Approximation for Bayesian Tensor Network Kernel Machines
Apr 29, 2026Albert Saiapin, Kim BatselierBayesian Neural Networks Gaussian Process
TimeMM: Time-as-Operator Spectral Filtering for Dynamic Multimodal Recommendation
Apr 29, 2026Wei Yang, Rui Zhong, Zihan Lin +4Multimodal Recommendation Kernel Method
Dictionary learning for Kernel EDMD
Apr 28, 2026Erik Lien Bolager, Boumediene Hamzi, Houman Owhadi +2Kernel Method Koopman Operator
Dynamic Regret for Online Regression in RKHS via Discounted VAW and Subspace Approximation
Apr 27, 2026Dmitry B. Rokhlin, Georgiy A. KarapetyantsKernel Hilbert Spaces Kernel Method
Identifiability and Stability of Generative Drifting with Companion-Elliptic Kernel Families
Apr 27, 2026HakGeun Lee, Hyonho ChunIdentifiability Kernel Method
Partition-of-Unity Gaussian Kolmogorov-Arnold Networks
Apr 26, 2026Amir NoorizadeganKolmogorov-Arnold Networks Kernel Method
Explicit integral representations and quantitative bounds for two-layer ReLU networks
Apr 25, 2026Anthony LeeRectified Linear Unit Networks Rectified Linear Unit

Jun 5, 2026 · cs.GRJ/K move · Enter open · S save

Frequency-Scale Saliency for Spectral Descriptor Analysis in 3D Shape Retrieval

Jianru Shen

University of Montana, Missoula, MT 59812, USA

Classical spectral descriptors such as the Heat Kernel Signature and Wave Kernel Signature are widely used for non-rigid 3D shape retrieval, yet their failure modes remain poorly understood. We present a frequency-scale saliency framework that audits these descriptors by quantifying the retrieval-level contribution of each descriptor scale interval through ablation. We introduce class spectral fingerprints to characterize category-level scale dependence, and show that descriptor similarity between class pairs is substantially correlated with retrieval failure, with a Spearman correlation of 0.479. Experiments on SHREC'11 demonstrate that short scales dominate retrieval performance while long scales are harmful, that HKS and WKS exhibit distinct scale dependence patterns, and that saliency-weighted retrieval improves mAP on hard categories by 0.156, with cross-fold and random-weight controls confirming that the gain is stable and not due to arbitrary reweighting.

Multiscale Kernel Method Saliency

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