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.

148 papers

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Online Adaptive Kernel Mixing for Gaussian Process Decision Making
Sep 17, 2026Kavin Aravindan, Mani Tej Sriram, Gautam Dasarathy +1Gaussian Process Kernel Method
A General Kernel Framework for Non-CND Distance Measures Using |D|-Dimensional Sparse Landmark Embeddings
Sep 16, 2026Marcus M. Noack, Maher B. Alghalayini, Mark D. RisserKernel Method Distance
Fast Learning Rates for Physics-Informed Kernel Methods
Sep 16, 2026Luc Brogat-Motte, Joachim Bona-Pellissier, Giacomo Meanti +1Physics-Informed Machine Learning Kernel Method
Kernel-Based Metrics Learning for Uncertain Opponent Vehicle Trajectory Prediction in Autonomous Racing
Sep 15, 2026Hojin Lee, Youngim Nam, Sanghun Lee +1Autonomous Racing Trajectory Prediction
A Weighted Kernel Method for Approximation that Adapts to Learned Multivariable Structure
Sep 15, 2026John E. Darges, Laura WeidensagerKernel Method Approximation
Physics Informed Random Feature Neural Networks for Solving PDEs
Sep 14, 2026Chi-An Chen, Chunyang Liao, Ming ZhongPartial Differential Equations Kernel Method
Low-Dimensional Embeddings for Gaussian Kernels on Manifolds
Sep 14, 2026Soumik Dutta, Kunal DuttaKernel Method Manifolds
Learning Interaction Kernels from Collective Steady States
Sep 10, 2026Baoli Hao, Mauro Maggioni, Ming ZhongSystem Identification Toward Interaction Dynamics
A Kernel-Based Modular Discriminant Analysis Framework for Small-Sample Learning
Sep 9, 2026Lingxiao Qu, Yan PeiKernel Method Principal Component Analysis
Muon-C: Operator-Aligned Muon for Convolutional Kernels
Sep 9, 2026Jiaxin Qing, Lexin LiConvolutional Muon
Revisiting Thinning Methods for Kernel Learning Problems
Sep 7, 2026Blanca Cano-Camarero, Yago R. Aguado-Carrillo-de-Albornoz, Ángela Fernández-Pascual +1Kernel Method Kernel Hilbert Spaces
MaxKernel: Agentic Kernel Generation for TPUs
Sep 7, 2026Shangkun Wang, Nina Cai, Charles Hoong +7Graphics Processing Unit Kernels Agentic Optimization
Learning Kernels by Alignment for Multiclass Bayes Classification
Sep 6, 2026Hollan Haule, Javier EscuderoMulticlass Classification Kernel Method
Geometry-Aware Graph Construction via Adaptive Spectral Bandwidth Control
Sep 3, 2026Ecem Bozkurt, Antonio OrtegaKernel Method Manifold Learning Method
Learning with Volterra Neural Networks: A System Theoretic Perspective
Sep 1, 2026Haoyu Yun, Hamid Krim, Yufang BaoNeural Tangent Kernel Kernel Method
Automated Maize Ear Phenotyping Using 3D Reconstructions
Sep 1, 2026Ritwesh A. Kumar, Som Tripathi, Peja Matthews +5Crop Key Vertebral Morphometry Variables
Sierpiński--Knopp Wasserstein Distance for Persistence Diagrams and Applications to 2-Wasserstein Approximation
Sep 1, 2026Sebastien Tchitchek, Julien TiernyWasserstein Distance Persistent Homology
DK-GBMKKM: Dynamic Kernel-Space Granular-Ball Multiple Kernel $k$ -Means ClusteringDK-GBMKKM: Dynamic Kernel-Space Granular-Ball Multiple Kernel k-Means Clustering
Sep 1, 2026Xiaoyu Lian, Yuchao Zhang, Shuyin Xia +2Granular-Ball Computing K-Means
Enhancing Bayesian Optimization and Active Learning Through Kernel Diversity
Aug 25, 2026Heng Zhang, Haotian Xiang, Konstantinos D. Polyzos +2Bayesian Optimization Hyperparameter Optimization
Adaptive $k$ Nearest Neighbors Classifier via Granular Ball ComputingAdaptive k Nearest Neighbors Classifier via Granular Ball Computing
Aug 13, 2026Xiaoyu Lian, Shuyin Xia, Hongxuan He +3Granular-Ball Computing Approximate Nearest-Neighbor Search
A Contract-Grade Verifier for LLM-Generated GPU Kernels, and a Native Blackwell Backward for the Gated-Linear-Recurrence Family
Aug 13, 2026Rishi Shah, Rishav ShresthaGraphics Processing Unit Kernels Correctness
Prof-K: Probabilistic One-Pass Filtering for Efficient Top-k Selection
Aug 12, 2026Tadeusz Dziarmaga, Witold Sikora, Łukasz Struski +2Top-K Optimal Sample Complexity
Kernel Methods for Learning Operators with Multiple Inputs and Outputs
Aug 12, 2026Adrien Weihs, Chunyang Liao, Jingmin Sun +1Probabilistic Operator Learning Kernel Method
Improving TensorSketch Using Complex Random Variables
Aug 11, 2026Amit Sharma, Mohammad Azhar Khan, Rameshwar Pratap +1Optimal Sample Complexity Kernel Method
Multi-kernel spectral clustering: Entrywise eigenvector perturbation bounds and exact recovery
Aug 9, 2026Zeqin Lin, Guangming Pan, Zhixiang Zhang +1Kernel Method Largest Eigenvalue
Kernel Methods for Refined Prophet Inequalities
Aug 9, 2026Patrick Loiseau, Mathieu Molina, Vianney Perchet +2Concentration Inequalities Worst-Case
Bridging the Gap Between Hyperdimensional Computing and Kernel Methods via the Nyström Method
Aug 7, 2026Quanling Zhao, Anthony Hitchcock Thomas, Ari Brin +2High-Dimensional Data Hypergraphs
SparseDitto: An Agentic Sparse Compilation Framework through Architecture-Aware Synthesis on GPUs
Aug 5, 2026Shiyang Li, Guangyan Sun, Jinwei Tang +3Graphics Processing Unit Kernels Sparsity
Random features for Grassmannian kernel approximation with bounded rank-one projections
Aug 4, 2026Rémi Delogne, Laurent JacquesKernel Method Approximation
Beyond the Gegenbauer Paradigm: q-Orthogonal Kernels for Machine Learning
Aug 4, 2026Álvaro Sánchez-Paniagua Ríos, Juan P. Llerena, Alberto Lastra +2Quantum Kernels Kernel Method
Sharp Root Anti-Concentration via Projective Incidence and Ordered Root Laws
Aug 3, 2026Zijun Wang, Yuchen Miao, Yifan Hu +1Concentration Inequalities O(T^Β)$ Simultaneous Regret
How fine a change can moments see? A scale law for detecting distribution shift, with a kernel calibration rule
Aug 2, 2026Adel KalechePersistent Homology Kernel Method
PIKS: Universal Physics-Informed Kernel Methods
Jul 29, 2026Joachim Bona-Pellissier, Giacomo Meanti, Matteo Santacesaria +1Physics-Informed Machine Learning Kernel Method
WGDnet: Wishart-guided Geometric-aware Deep Network for PolSAR Image Classification
Jul 26, 2026Junfei Shi, Haojia Zhang, Yu Cheng +1Synthetic Aperture Radar Geometric Deep Learning
Deep Convolutional Large-Margin $\ell_p$ -SVDD for Visual Anomaly DetectionDeep Convolutional Large-Margin \ell_p-SVDD for Visual Anomaly Detection
Jul 24, 2026Alireza Dastmalchi Saei, Shervin Rahimzadeh ArashlooReconstruction-Based Anomaly Detection Anomaly Detection
Directional Kernel Mean Difference: A Fast Signed Statistic for Univariate Distribution Comparison
Jul 22, 2026Shijie Zhong, Jiangfeng FuMaximum Mean Discrepancy Kernel Method
Nonlinear Bias-Compensated Adaptive Filter and Its Application for Time-Series Prediction
Jul 22, 2026Yi Peng, Haiquan Zhao, Jinhui HuMultivariate Forecasting Noise-Aware
A Multiclass Quantum Aligned Centroid Kernel
Jul 22, 2026Kilian Tscharke, Pascal DebusQuantum Kernels Qubit
Unsupervised Multi-kernel Learning for Automated Algorithm Selection
Jul 21, 2026Yihang Lu, Tome Eftimov, Carola DoerrKernel Method Black-Box Optimization
Information-Based Exploration via Random Features for Reinforcement Learning
Jul 20, 2026Waris Radji, Odalric-Ambrym MaillardEfficient Exploration Autonomous Exploration Strategies
Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones
Jul 19, 2026Ayoub Ghriss, Sourav ChakrabortyQuadratic Attention Dense Associative Memory
TraceSynth: Generating Production-Quality Kernel Traces with Constraint-Guided Diffusion Models
Jul 13, 2026Yuvraj Sehgal, Sneh Patel, Mahsa Panahandeh +2Circuit Tracing Inference Workloads
Neural Discovery of Memory and Nonlocal Kernels in Integro-Differential Equations with Constrained Kolmogorov--Arnold Networks
Jul 13, 2026Aruzhan Tleubek, Salah A FaroughiKolmogorov-Arnold Networks Kernel Method
DSSMs: State Space Models with Explicit Memory via Delay Differential Equations
Jul 11, 2026Yixiao Qian, Song Chen, Jiaxu Liu +2State Space Models Sequence Modeling
Group Invariant Spectral Embedding
Jul 9, 2026Yeari Vigder, Paulina Hoyos, David Thong +3Graph Laplacians Symmetry
Learning-based Physics-Constrained Neural Kernel for Sound Field Estimation With Source-Position-Dependent Directional Weighting
Jul 7, 2026Mattia Marella, Shoichi KoyamaSound Source Localization Kernel Method
Sensitivity Sampling with Predictions for k-Means Clustering
Jul 6, 2026Cristian Boldrin, Fabio VandinK-Means Kernel Method
Non-Asymptotic Error Bounds for SMC with Biased Proposals: Application to Conditional Diffusion Sampling
Jul 6, 2026Stanislas Strasman, Gabriel Victorino Cardoso, Sylvain Le Corff +2Diffusion Sampling Monte Carlo
A Kernel Fisher Discriminant Analysis-Based Tree Ensemble Classifier: KFDA Forest
Jun 27, 2026Donghwan Kim, Seung Hwan Park, Jun-Geol BaekRandom Forest Decision Trees
Enhancing Numerical Prediction in LLMs via Smooth MMD Alignment
Jun 26, 2026Zhuo Zuo, Li Yue, Wenhao Zheng +2Maximum Mean Discrepancy Kernel Method
Differential Spectral Damping Gap Adaptive Regularization for Ill-Conditioned Kernel Methods
Jun 22, 2026Praveg VashishthaKernel Method Singular Value Decomposition
Orthogonal Discrepancy Kernels for Learning with Partial Physics
Jun 19, 2026Swapnil Manna, Timothy J. Rogers, Lawrence BullSystem Identification Discrepancy
Effective Dimension Governs Generalization in Quantum Kernel Vision Models
Jun 18, 2026Jian Xu, Delu Zeng, John Paisley +1Quantum Kernels Entanglement
Spectral DPPs via NEPv: A Scalable Continuous Relaxation of Determinantal MAP for Diversity-Aware Data Selection
Jun 17, 2026Richard Yi Da XuLargest Eigenvalue Kernel Method
Kernel of Partition Paths: A Unified Representation for Tree Ensembles
Jun 17, 2026Nicolas MahlerDecision Trees Kernel Method
Scalable Pairwise Kernel Learning with Stochastic Vec Trick
Jun 15, 2026Napsu Karmitsa, Tapio Pahikkala, Antti AirolaKernel Method
Flexible Kernels for Protein Property Prediction
Jun 9, 2026Martin Jankowiak, Yerdos Ordabayev, Rudraksh Tuwani +4Protein Data Bank Protein Design
Generalized-CVO: Fast and Correspondence-Free Local Point Cloud Registration with Second Order Riemannian Optimization
Jun 8, 2026Ray Zhang, Marcus Greiff, Thomas Lew +1Point Cloud Registration 3D Single Object Tracking
Orthogonality and Dimensionality in Airline Cluster Analysis using PCA and Kernel PCA
Jun 6, 2026Andreas SchlapbachPrincipal Component Analysis Cluster Structure
How Deep Are Deep GPs, Really? A Sharp Threshold and a Non-Gaussian Limit for Compositional GPs
Jun 6, 2026Mark Kozdoba, Shie MannorGaussian Process Central Limit Theorem

Sep 17, 2026 · cs.LGJ/K move · Enter open · S save

Online Adaptive Kernel Mixing for Gaussian Process Decision Making

Kavin Aravindan, Mani Tej Sriram, Gautam Dasarathy, Tejas Bodas

Gaussian Processes (GPs) are widely used as surrogates for black-box functions in sequential decision-making problems such as Bayesian optimization (BO), level set estimation (LSE), and Bayesian active learning (BAL). GP performance critically depends on kernels, and standard kernels can lead to suboptimal decisions under misspecification. To address this, we introduce HACK GPs (Hedge Adaptive Cumulative Kernels), a method that views kernel selection as an online learning with expert advice problem. HACK treats each candidate kernel as a GP "expert" and updates a distribution over experts online using AdaHedge, based on a loss received as a proxy for their ability to fit the function and align with the task objective. We provide two variants of HACK: (i) Mixture of Gaussians (MoG) and (ii) categorical sampling. We establish general guarantees showing that, under a loss-gap condition, the weight concentrates on the best kernel and the resulting acquisition function is close to that of the best expert. Empirically, we observe robust performance across BO, LSE, and BAL compared to standard kernels such as Squared Exponential and Matern-5/2, as well as simple ensemble baselines.

Gaussian Process Kernel Method Bayesian Optimization Learning-Augmented Algorithms Generalized Linear Bandit Hyperparameter Optimization Active Learning Onboard Global Navigation Satellite System Ground Truth

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