Kernel Methods

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

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Measuring Differences between Conditional Distributions using Kernel Embeddings
May 4, 2026Peter Moskvichev, Siu Lun Chau, Dino SejdinovicKernel Mean Embeddings Maximum Mean Discrepancy
School of Mathematical Sciences, Adelaide University · College of Computing and Data Science, Nanyang Technological University
A Semi-Supervised Kernel Two-Sample Test
May 3, 2026Gyumin Lee, Shubhanshu Shekhar, Ilmun KimTwo-Sample Testing Kernel Methods
Dept. of Statistics Pennsylvania State University · Dept. of EECS University of Michigan · Dept. of Mathematical Sciences KAIST
Complex Diffusion Maps with $ω$ -Parameterized Kernels Revealing Inherent Harmonic Representations
May 3, 2026Tongzhen Dang, Weiyang Ding, Michael K. NgManifold Learning Kernel Methods
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China. · Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China. · Department of Mathematics, Hong Kong Baptist University, Hong Kong, China.
New Bounds for Kernel Sums via Fast Spherical Embeddings
May 2, 2026Tal WagnerKernel Methods
Tel Aviv University
Kernel Affine Hull Machines as Compute-Efficient Encoders for Frozen Semantic Spaces
May 1, 2026Mohit Kumar, Somayeh Kargaran, Bernhard A. Moser +1Legal IR Kernel Methods
University of Rostock, Germany · Software Competence Center Hagenberg GmbH, Austria Hagenberg, Austria
Deep Kernel Learning for Stratifying Glaucoma Trajectories
May 1, 2026Bruce Rushing, Angela Danquah, Alireza Namazi +2Clinical Outcome Prediction Kernel Methods
Research Computing, University of Virginia · School of Data Science, University of Virginia · School of Medicine, University of Virginia
Laplace Approximation for Bayesian Tensor Network Kernel Machines
Apr 29, 2026Albert Saiapin, Kim BatselierUncertainty Quantification Bayesian Inference
1Delft Center for Systems and Control, Delft University of Technology
Dictionary learning for Kernel EDMD
Apr 28, 2026Erik Lien Bolager, Boumediene Hamzi, Houman Owhadi +2Koopman Operator Learning Nonlinear System Identification
School of Computation, Information and Technology, Munich Data Science Institute, Munich Center for Machine Learning, Technical University of Munich, Germany · Department of Computing and Mathematical Sciences, Caltech, USA · The Alan Turing Institute, UK +1
Dynamic Regret for Online Regression in RKHS via Discounted VAW and Subspace Approximation
Apr 27, 2026Dmitry B. Rokhlin, Georgiy A. KarapetyantsSequential Decision Making Reproducing Kernel Hilbert Spaces
Institute of Mathematics, Mechanics and Computer Sciences of the Southern Federal University
Identifiability and Stability of Generative Drifting with Companion-Elliptic Kernel Families
Apr 27, 2026HakGeun Lee, Hyonho ChunGenerative Modeling Convergence Analysis
Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
Generalising maximum mean discrepancy: kernelised functional Bregman divergences
Apr 27, 2026Russell Tsuchida, Frank NielsenKernel Mean Embeddings Functional Bregman Divergences
Department of Data Science and AI, Monash University, Australia. · Sony Computer Science Laboratories, Inc. Tokyo, Japan.
Partition-of-Unity Gaussian Kolmogorov-Arnold Networks
Apr 26, 2026Amir NoorizadeganKolmogorov-Arnold Networks Kernel Methods
Department of Mathematics, Hong Kong Baptist University, Hong Kong SAR, China
Complex Stochastic Gradient Descent and Directional Bias in Reproducing Kernel Hilbert Spaces
Apr 24, 2026Natanael Alpay, Emeric BattagliaKernel Regression Reproducing Kernel Hilbert Spaces
A Kernel Nonconformity Score for Multivariate Conformal Prediction
Apr 23, 2026Louis Meyer, Wenkai XuConformal Prediction Kernel Methods
University of Warwick
Deep kernel video approximation for unsupervised action segmentation
Apr 23, 2026Silvia L. Pintea, Jouke DijkstraTemporal Action Segmentation Kernel Methods
Department of Intellgient Systems (DIS), Tilburg University, Tilburg, NL · Division of Image Processing, LUMC, Leiden, NL
Benchmarking Quantum Kernel Support Vector Machines Against Classical Baselines on Tabular Data: A Rigorous Empirical Study with Hardware Validation
Apr 20, 2026Siavash Kakavand, Christoph Strohmeyer, Michael SchlotterQuantum Feature Maps Quantum Kernel Methods
SHARE at FAU, Schaeffler Technologies AG & Co. KG, Herzogenaurach 91074, Bavaria, Germany
UniCon: Unified Framework for Efficient Contrastive Alignment via Kernels
Apr 17, 2026Hangke Sui, Yuqing Wang, Minh N DoContrastive Learning Cross-Modal Alignment
Department of Electrical & Computer Engineering, The Grainger College of Engineering, UIUC · Coordinated Science Laboratory, University of Illinois Urbana-Champaign (UIUC) · Siebel School of Computing and Data Science, The Grainger College of Engineering, UIUC +1
Structural interpretability in SVMs with truncated orthogonal polynomial kernels
Apr 16, 2026Víctor Soto-Larrosa, Nuria Torrado, Edmundo J. HuertasInterpretable ML Support Vector Machines
Dpto. de Biociencias, Facultad de Ciencias Biomédicas y de la Salud, Universidad Europea de Madrid, C/ Tajo, s/n, 28670 Villaviciosa de Odón, Madrid, Spain. · Dpto. de Matemáticas, Facultad de Ciencias, Universidad Autónoma de Madrid, C/ Francisco Tomás y Valiente, 7, 28049 Madrid, Spain. · Instituto de Ciencias Matemáticas (ICMAT), Campus de Cantoblanco UAM, C/ Nicolás Cabrera, 13–15, 28049 Madrid, Spain. +1
KVNN: Learnable Multi-Kernel Volterra Neural Networks
Apr 16, 2026Haoyu Yun, Hamid Krim, Yufang BaoEfficient Neural Network Inference Kernel Methods
Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, USA · Department of Mathematics and Computer Science, Fayetteville State University, Fayetteville, NC, USA
Breaking Data Symmetry is Needed For Generalization in Feature Learning Kernels
Mar 31, 2026Marcel Tomàs Bernal, Neil Rohit Mallinar, Mikhail BelkinSymmetry Breaking Kernel Methods
CFIS UPC · UC San Diego
Optimal uncertainty bounds for multivariate kernel regression under bounded noise: A Gaussian process-based dual function
Mar 17, 2026Amon Lahr, Anna Scampicchio, Johannes Köhler +1Kernel Methods Gaussian Process Regression
Institute for Dynamical Systems and Control, ETH Zurich, Zurich, Switzerland
Margin in Abstract Spaces
Mar 7, 2026Yair Ashlagi, Roi Livni, Shay Moran +1Kernel Methods Statistical Learning Theory
School of Electrical and Computer Engineering, Tel Aviv University. · Departments of Mathematics, Computer Science, and Data and Decision Sciences, Technion, and Google Research. · Departments of Mathematics, Technion.
Regular Fourier Features for Nonstationary Gaussian Processes
Feb 26, 2026Arsalan Jawaid, Abdullah Karatas, Jörg SeewigFourier Feature Embeddings Gaussian Processes
Institute of Measurement and Sensor Technology, University of Kaiserslautern-Landau, Kaiserslautern, Germany · Independent Researcher, Mannheim, Germany
Generative Modeling via Kernelized Stochastic Interpolants
Feb 23, 2026Florentin Coeurdoux, Etienne Lempereur, Nathanaël Cuvelle-Magar +2Stochastic Differential Equations Stochastic Interpolants
Capital Fund Management, Paris, France · Département d’informatique, ENS, Université PSL, Paris, France · Collège de France, Paris, France +2
Mechanistic Evidence for Spectral Structures in Prior-Data Fitted Networks
Jan 29, 2026Kaustubh Sharma, Srijan Tiwari, Ojasva Nema +1Prior-Data Fitted Networks Neural Network Interpretability
Department of Electrical Engineering, Indian Institute of Technology Roorkee (IIT Roorkee), Uttarakhand, India · Department of Metallurgical and Materials Engineering, Indian Institute of Technology Roorkee (IIT Roorkee), Uttarakhand, India
Finite-Sample Unbiased Variance of MMD under Unbalanced Sampling: Exact Estimation and Quasi-Linear Computation
Jan 20, 2026Shijie Zhong, Yikun Yang, Da Gong +1Maximum Mean Discrepancy Kernel Methods
School of Power and Energy, Northwestern Polytechnical University, 1 Dongxiang Road, Xi’an, 710129, Shanxi, China. · Advanced Power Research Institute, Northwestern Polytechnical University, 619 Jicui Road, Chengdu, 610299, Sichuan, China. · Taihang Laboratory, Chengdu, 610200, Sichuan, China. +1
Minimum distance classification for nonlinear dynamical systems
Jan 7, 2026Dominique MartinezDynamical Systems Time Series Classification
Aix-Marseille University, CNRS, ISM, 13009 Marseille, France
Computationally efficient goodness-of-fit tests through kernelized Stein discrepancy
Dec 23, 2025Zhihan Huang, Ziang NiuKernel Methods Statistical Hypothesis Testing
Department of Statistics and Data Science University of Pennsylvania
Sparse, self-organizing ensembles of local kernels detect rare statistical anomalies
Nov 5, 2025Gaia Grosso, Sai Sumedh R. Hindupur, Thomas Fel +3Kernel Methods Statistical Learning Theory
NSF AI Institute for Artificial Intelligence and Fundamental Interactions, Cambridge, 02139, MA · Laboratory for Nuclear Science, Massachusetts Institute of Technology, Cambridge, 02139, MA · School of Engineering and Applied Sciences, Harvard University, Allston, 02134, MA +1
Theoretical Refinement of CLIP by Utilizing Linear Structure of Optimal Similarity
Oct 17, 2025Naoki Yoshida, Satoshi Hayakawa, Yuhta Takida +3Cross-Modal Learning Kernel Mean Embeddings
The University of Tokyo · Sony Group Corporation · Sony AI

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Measuring Differences between Conditional Distributions using Kernel Embeddings

Peter Moskvichev, Siu Lun Chau, Dino Sejdinovic

School of Mathematical Sciences, Adelaide University · College of Computing and Data Science, Nanyang Technological University

Kernel Mean Embeddings Maximum Mean Discrepancy Kernel Methods

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Comparing conditional distributions is a fundamental challenge in statistics and machine learning, with applications across a wide range of domains. While proposed methods for measuring discrepancies using kernel embeddings of distributions in a reproducing kernel Hilbert space (RKHS) provide powerful non-parametric techniques, the existing literature remains fragmented and lacks a unified theoretical treatment. This paper addresses this gap by establishing a coherent framework for studying kernel-based methods to measure divergence between conditional distributions through what we refer to as conditional maximum mean discrepancy (CMMD). The CMMD consists of a family of metrics which we call levels, with three special cases each using a different type of RKHS embedding: CMMD

_0

(conditional mean operators), CMMD

_1

(conditional mean embeddings), and CMMD

_2

(joint mean embeddings). We additionally introduce a general level

s

CMMD, clarifying the required assumptions, and establishing mathematical connections between the levels through the lens of operator-based smoothing. In addition to reviewing previously proposed estimators, we introduce a novel doubly robust estimator for the CMMD that maintains consistency provided at least one of the underlying models is correctly specified. We provide numerical experiments demonstrating that the CMMD effectively captures complex conditional dependencies for statistical testing.

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