Kernel Methods

Follow this topic Ask about this topic

Momentum

18 papers in the last four weeks, up 125% on the four weeks before. 0.2% of all new papers.

Jul 13Week of Sep 28

Latest papers 168

All topics

Sequential Kernel-based Conditional Independence Testing via Adaptive Betting
Jun 17, 2026Zheng He, Danica J. SutherlandKernel Methods Conditional Independence Testing
Department of Computer Science, University of British Columbia, Vancouver, Canada · Alberta Machine Intelligence Institute, Edmonton, Canada
Kernel of Partition Paths: A Unified Representation for Tree Ensembles
Jun 17, 2026Nicolas MahlerFeature Attribution Ensemble Learning
Datapred SAS 23 rue Mirabeau 94300 Vincennes, France · Datapred SA EPFL Innovation Park – Bâtiment A 1015 Lausanne, Switzerland
Scalable Pairwise Kernel Learning with Stochastic Vec Trick
Jun 15, 2026Napsu Karmitsa, Tapio Pahikkala, Antti AirolaKernel Methods
Department of Computing, University of Turku, FI-20014 Turku, Finland
Contrastive-Difference CKA Reveals Concept-Specific Structural Alignment Across Language Model Architectures
Jun 15, 2026Xueping GaoKernel Methods Representation Geometry in Language Models
Alibaba Cloud
Brownian Kernel Ladders
Jun 14, 2026Mahdi Mohammadigohari, Giuseppe Di Fatta, Giuseppe Nicosia +1Hierarchical Representation Learning Reproducing Kernel Hilbert Spaces
Faculty of Engineering, Free University of Bozen-Bolzano, Via Bruno Buozzi 1, Bolzano, 39100, Italy · Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, 95123, Santa Sofia, 97, Italy · Center for Applied Optimization, Department of Industrial and Systems Engineering, University of Florida, FL 32611, Gainesville, USA
Flexible Kernels for Protein Property Prediction
Jun 9, 2026Martin Jankowiak, Yerdos Ordabayev, Rudraksh Tuwani +4Kernel Methods Molecular Property Prediction
Generate Biomedicines, Somerville, MA, USA
Bernstein-Schur Kernels: Random Features by Sketched Modulation and Radial Randomization
Jun 8, 2026Taha BouhsineFourier Feature Embeddings Kernel Ridge Regression
Azetta AI
Generalized-CVO: Fast and Correspondence-Free Local Point Cloud Registration with Second Order Riemannian Optimization
Jun 8, 2026Ray Zhang, Marcus Greiff, Thomas Lew +1Riemannian Optimization LiDAR Odometry
Toyota Research Institute, 4440 El Camino Real, Los Altos, California, USA
TorchKM: A GPU-Oriented Library for Kernel Learning and Model Selection
Jun 4, 2026Yikai Zhang, Gaoxiang Jia, Jie Ding +1Model Selection Kernel Regression
Department of Statistics and Actuarial Science, University of Iowa, Iowa City, IA 52242, USA · Individual Researcher · AIScientists, Inc. (MorphMind), 245 Main Street, Cambridge, MA 02142, USA
KODA: Contrastive Representation Comparison and Alignment for Vision-Language Foundation Models
Jun 2, 2026Youqi Wu, Mohammad Jalali, Farzan FarniaKernel Methods Multimodal Foundation Models
Department of Computer Science and Engineering, The Chinese University of Hong Kong.
Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials
Jun 2, 2026Joanna Zou, Fraser Birks, Dallas Foster +1Machine Learning Interatomic Potentials Active Learning
Center for Computational Science & Engineering, Schwarzman College of Computing, MIT · Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick · NVIDIA
Analytical Evaluation of DCA Convergence Properties for Minimizing Prediction Functions of Gaussian RBF Support Vector Regression
Jun 2, 2026Yohei Kakimoto, Yuto Omae, Hirotaka TakahashiOptimization Convergence Analysis Support Vector Machines
Nihon University · Tokyo City University
AdaKernel: Learning Adaptive Kernel Parameters for Spatiotemporal Graph Neural Networks
May 31, 2026Zhongyue Zhang, Guangyin Jin, Yuxuan Liang +2Kernel Methods Spatiotemporal Forecasting
Sichuan University · PLA Academy of Military Science · The Hong Kong University of Science and Technology (Guangzhou)
Eigen-Spike Emergence and Quadratic Equivalents for Conjugate Kernels on Nonlinearly Separable Data
May 28, 2026Collin Cranston, Zhichao Wang, Todd Kemp +1Random Matrix Theory Kernel Methods
Department of Mathematics University of California, San Diego, USA · ICSI and Department of Statistics University of California, Berkeley, USA · ICSI, LBNL and Department of Statistics University of California, Berkeley, USA
Supervised Distributional Reduction via Optimal Transport and Dependence Maximization
May 26, 2026Sai-Aakash Ramesh, Archit Sood, Andrew Corbett +1Kernel Methods Dimensionality Reduction
digiLab, UK · University of Bristol, UK
Semiparametrically Efficient Inference for Kernel Measures of Noise Heterogeneity
May 26, 2026Jakub Wornbard, Zikai Shen, Dimitri Meunier +1Semiparametric Inference Kernel Methods
University College London
Nonlinear Data Integration via Kernel Methods for Data Collaboration Analysis
May 26, 2026Yamato Suetake, Yuta Kawakami, Shunnosuke Ikeda +1Privacy-Preserving ML Kernel Methods
Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Ibaraki, Japan · Institute of Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki, Japan
Near-Optimal Regret in Adversarial Kernel Bandits
May 26, 2026Yu-Jie Zhang, Hao Qiu, Jonathan Scarlett +1Kernel Methods Adversarial Bandits
National University of Singapore
Conditional KRR: Injecting Unpenalized Features into Kernel Methods with Applications to Kernel Thresholding
May 25, 2026Rustem Takhanov, Zhenisbek AssylbekovKernel Ridge Regression Kernel Regression
Department of Mathematics, Nazarbayev University, Astana, Kazakhstan · Nazarbayev University Research Administration, Astana, Kazakhstan · Purdue University Fort Wayne, Indiana, USA.
Branched Signature Kernel Solvers for ODEs with rough Single-Trajectory signals
May 25, 2026Munawar Ali, Qi Feng, Charlie Pyle +1Kernel Methods
Department of Mathematics, Florida State University, Tallahassee, FL 32306 · Department of Mathematics, Texas A&M University, College Station, TX 77843 · Department of Mathematics, Rutgers University, Piscataway, NJ 08854
LAPLEX: The FFT of Learnable Laplace Kernels
May 23, 2026Łukasz Struski, Hanna Blazhko, Piotr Kubaty +1Efficient Neural Network Inference Kernel Methods
Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland · Doctoral School of Exact and Natural Sciences, Jagiellonian University, Kraków, Poland · Centre for Credible Artificial Intelligence, Warsaw University of Technology, Poland
A Martingale Kernel Independence Test
May 21, 2026Felix Laumann, Zhaolu Liu, Mauricio BarahonaMaximum Mean Discrepancy Kernel Methods
Imperial College London
Adaptive Measurement Allocation for Learning Kernelized SVMs Under Noisy Observations
May 21, 2026Artur MiroszewskiAdaptive Sampling Support Vector Machines
Φ-lab, European Space Agency (ESA/ESRIN), Frascati, Italy
Beyond Scalar Objectives: Expert-Feedback-Driven Autonomous Experimentation for Scientific Discovery at the Nanoscale
May 20, 2026Ralph Bulanadi, Jefferey Baxter, Arpan Biswas +5Pairwise Preference Learning Active Learning
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA · University of Tennessee-Oak Ridge Innovation Institute, University of Tennessee, Knoxville, TN 37996, USA · Department of Material Science and Engineering, School of Materials and Chemical Technology, Institute of Science Tokyo, Yokohama, 226-8502, Japan +3
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 Kernel Methods
Department of Mathematics “Giuseppe Peano”, University of Torino, via Carlo Alberto 10, 10123 Torino, Italy · Member of the INdAM Research Group GNCS · Department of Mathematics, Hong Kong Baptist University, Hong Kong SAR, China
Spherical Harmonic Optimal Transport: Application to Climate Models Comparisons
May 18, 2026Pierre Houédry, Iskander Legheraba, Léo Buecher +1Kernel Methods Optimal Transport
INRIA Rennes · LPHI, UMR 5294, CNRS, INSERM · IRISA, UMR 6074, CNRS
Automated Kernel Discovery Towards Understanding High-dimensional Bayesian Optimization
May 18, 2026Taeyoung Yun, Woocheol Shin, Inhyuck Song +2Bayesian Optimization Large Language Model-Guided Optimization
1Korea Advanced Institute of Science and Technology (KAIST)
How does feature learning reshape the function space?
May 18, 2026João Lobo, Bruno Loureiro, Long Tran-Than +1Representation Learning Kernel Methods
Department of Computer Science, University of Warwick, United Kingdom · Departement d’Informatique, École Normale Supérieure, PSL & CNRS · School of Mathematical Sciences, Institute of Natural Sciences and MOE-LSC, Shanghai Jiao Tong University, China. Part of work was done at Department of Computer Science, and Centre for Discrete Mathematics and its Applications (DIMAP), University of Warwick, United Kingdom
From Layers to Networks: Comparing Neural Representations via Diffusion Geometry
May 15, 2026Atharva Khandait, Jan E. GerkenRepresentational Similarity Analysis Neural Representation Geometry
Department of Mathematical Sciences, Chalmers University of Technology and the University of Gothenburg, SE-412 96 Gothenburg, Sweden.
AQKA: Active Quantum Kernel Acquisition Under a Shot Budget
May 14, 2026Jian Xu, Chao Li, Delu Zeng +2Kernel Regression Active Learning
1RIKEN iTHEMS · 2RIKEN AIP · 3South China University of Technology +1

Jun 17, 2026, stat.MLJ/K move · Enter open · S save

Sequential Kernel-based Conditional Independence Testing via Adaptive Betting

Zheng He, Danica J. Sutherland

Department of Computer Science, University of British Columbia, Vancouver, Canada · Alberta Machine Intelligence Institute, Edmonton, Canada

Kernel Methods Conditional Independence Testing Sequential Hypothesis Testing Statistical Hypothesis Testing

Open paper AskPDF arXiv

Testing conditional independence is fundamental yet intrinsically difficult: without additional assumptions, Type I error control is impossible in general. The "Model-X'' paradigm addresses this difficulty by assuming exact knowledge of a relevant conditional distribution. While small deviations from this assumption can sometimes be tolerated in classical one-shot testing, existing sequential conditional independence tests typically require the Model-X conditional to be known exactly, making them fragile when it must instead be estimated. We propose a new approach that is substantially more robust to such estimation error. Our method applies testing-by-betting to an adaptively optimized Kernel Conditional Independence statistic, together with a normalization scheme and a truncate-and-shift calibration strategy. These modifications greatly reduce Type I error inflation while preserving high power across high-dimensional synthetic benchmarks and real-world fairness tasks, outperforming existing sequential Model-X approaches. Code is available at https://github.com/he-zh/SKCI.

Momentum

Related topics

Latest papers 168