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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Bilevel optimization for data-driven learning of Koopman embeddings using kernel-based autoencoders
Oct 8, 2026Joel-Pascal Ntwali N'konzi, Feliks Nüske, Stefan KlusKoopman Operator Learning Latent Dynamics Modeling
Maxwell Institute for Mathematical Sciences, The University of Edinburgh & Heriot–Watt University, Edinburgh, United Kingdom · Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany · LAAS-CNRS, Université de Toulouse, Toulouse, France +1
Using Weisfeiler-Leman Features for Algorithm Selection in Constraint Optimisation
Oct 8, 2026Alessio Pellegrino, Jacopo MauroAlgorithm Selection Constraint Programming
University of Southern Denmark, Denmark
Conditional Kernel Stein Discrepancy
Oct 8, 2026Federico Matteucci, Florian KalinkeKernel Methods Statistical Hypothesis Testing
Karlsruhe Institute of Technology
Feature Space Adaptation for Effortless Gaussian Process Flows
Oct 8, 2026Thomas Cowperthwaite, Louis Sharrock, Lachlan Astfalck +1Gaussian Processes Bayesian Inference
University of Cambridge · University College London · University of New South Wales +1
Kernel Autoresearch for Open-Ended Model Discovery
Oct 7, 2026Richard Cornelius Suwandi, Feng Yin, Kevin MurphyKernel Methods Automated Algorithm Discovery
CUHK-Shenzhen · University of British Columbia
Sharp Asymptotic Theory of Maximum Likelihood Estimation for Gaussian Processes with an RBF Kernel
Oct 7, 2026Ameer Qaqish, Didong LiGaussian Processes Kernel Methods
Department of Biostatistics, University of North Carolina at Chapel Hill
Unbounded Characteristic and Universal Kernels
Oct 7, 2026Jose Cribeiro-Ramallo, Florian Kalinke, Zoltán SzabóKernel Methods Reproducing Kernel Hilbert Spaces
Karlsruhe Institute of Technology · London School of Economics
Second-order optimization of variable projection SVM models and road abnormality detection
Oct 7, 2026Andrea Angino, Matthias Voigt, Rolf Krause +1Second-Order Optimization Support Vector Machines
Faculty of Mathematics and Computer Science, UniDistance Suisse, Brig, Switzerland · CEMSE Division, KAUST, Thuwal, Saudi Arabia · Eötvös Loránd University, Faculty of Informatics, Department of Numerical Analysis
Bayesian Optimization on Function Spaces via Sparse RKHS Manifolds
Oct 5, 2026Davide Sartor, Meghan E. Huber, Donghyun Kim +1Bayesian Optimization Black-Box Optimization
Department of Mathematics and Statistics University of Massachusetts Amherst · Department of Mechanical and Industrial Engineering University of Massachusetts Amherst · Manning College of Information and Computer Sciences University of Massachusetts Amherst
MercerFlow: Flow Matching in a Kernel-Induced Latent Space for Probabilistic Forecasting
Oct 5, 2026Ilya Kuleshov, Egor Serov, Alexey ZaytsevMultivariate Time Series Forecasting Conditional Flow Matching
Applied AI Institute Moscow, Russia
Locality Sensitive Hashing for p-Exponential Kernels with Applications to Density Estimation
Oct 4, 2026Barak Gorodissky, Tal WagnerKernel Methods
Tel Aviv University
In-context Learning of Single-index Targets: Comparing Kernel and Feature Learners
Oct 1, 2026Haotian Gu, Yizhou Xu, Lenka ZdeborováRepresentation Learning Single-Index Models
Statistical Physics of Computation Laboratory, École Polytechnique Fédérale de Lausanne (EPFL) · University of Chinese Academy of Sciences · Information, Learning and Physics Laboratory, École Polytechnique Fédérale de Lausanne (EPFL)
Certified Approximation for Interpretable Representer Landmarks
Sep 30, 2026Jayanta Mukherjee, Shourya Verma, Mengbo Wang +2Neural Network Interpretability Kernel Methods
Department of Computer Science Purdue University West Lafayette, IN 47907, USA
AdaKerNet: Neural Kernel Decoding for Task-Adaptive Prediction with Multimodal Large Models
Sep 28, 2026Konstantinos D. Polyzos, Eleni Oikonomou, Tara JavidiMultimodal Large Language Models Kernel Methods
University of California San Diego
Subgroup Rank-1 Lattice for Practical High-dimensional Black-box Integral Approximation
Sep 28, 2026Yueming LyuKernel Mean Embeddings Kernel Methods
Centre for Frontier AI Research (CFAR) Agency for Science, Technology and Research (A*STAR) 1 Fusionopolis Way #16-16 Connexis Singapore, 138632
Single-Layer MeMo as a Randomized Hamming-Kernel Classifier
Sep 28, 2026Alessandro StraziotaMemory-Augmented Language Models Next-Token Prediction
Department of Enterprise Engineering University of Rome “Tor Vergata” Rome, Italy
Deep kernel hedging
Sep 28, 2026Jean-Loup Dupret, Donatien Hainaut, Edouard MotteQuantitative Finance Reproducing Kernel Hilbert Spaces
University of Amsterdam, Amsterdam School of Economics · ETH Zurich, Department of Mathematics, RiskLab · Université Catholique de Louvain, LIDAM–ISBA
Online Adaptive Kernel Mixing for Gaussian Process Decision Making
Sep 17, 2026Kavin Aravindan, Mani Tej Sriram, Gautam Dasarathy +1Bayesian Optimization Active Learning
International Institute of Information Technology, Hyderabad · Arizona State University
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 Methods Gaussian Process Regression
Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory Berkeley, CA 94720, USA · Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory Berkeley, CA 94720, USA
Kernel-Based Metrics Learning for Uncertain Opponent Vehicle Trajectory Prediction in Autonomous Racing
Sep 15, 2026Hojin Lee, Youngim Nam, Sanghun Lee +1Multi-Agent Trajectory Prediction Autonomous Racing
Department of Mechanical Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea
Geometry of learning dynamics: Gradient descent versus natural gradient on the ridge of optimization
Sep 15, 2026Akira TamamoriInformation Geometry Gradient Descent
Faculty of Information Science, Aichi Institute of Technology 1247 Yachigusa, Yakusa-cho, Toyota-shi, Aichi 470-0392, Japan
A Weighted Kernel Method for Approximation that Adapts to Learned Multivariable Structure
Sep 15, 2026John E. Darges, Laura WeidensagerKernel Regression Kernel Methods
Department of Mathematics, Emory University · Department of Mathematics, Simon Fraser University
Low-Dimensional Embeddings for Gaussian Kernels on Manifolds
Sep 14, 2026Soumik Dutta, Kunal DuttaFourier Feature Embeddings Kernel Methods
Institute of Informatics, University of Warsaw, Poland
A Kernel-Based Modular Discriminant Analysis Framework for Small-Sample Learning
Sep 9, 2026Lingxiao Qu, Yan PeiKernel Methods Data-Efficient Learning
Graduate School of Computer Science and Engineering, University of Aizu, 90 Kamiiawase, Tsuruga, Ikki-machi, Aizuwakamatsu, 965-8580, Fukushima, Japan · Computer Science Division, University of Aizu, 90 Kamiiawase, Tsuruga, Ikki-machi, Aizuwakamatsu, 965-8580, Fukushima, Japan
Exact Degeneracy Under Balanced k-Shot Sampling:Consequences for Small-Sample Discriminant Analysis on LLM Embeddings
Sep 9, 2026Lingxiao QuFew-Shot Learning Kernel Methods
The University of Aizu, Aizuwakamatsu, Fukushima, Japan
MiNCE: Nonparametric, Strongly Consistent Confidence Envelopes for Band-Limited Functions and their Smoothed Spectra
Sep 8, 2026Balázs Csanád Csáji, Bálint HorváthConfidence Region Estimation Nonparametric Regression
HUN-REN SZTAKI: Institute for Computer Science and Control, Budapest, Hungary · Institute of Mathematics, Eötvös Loránd University (ELTE), Budapest, Hungary
Heat Kernel Textures: the Geodesic Gaussians That Do Not Splat
Sep 7, 2026Simone Foti, Caner Korkmaz, Stefanos Zafeiriou +1Kernel Methods
Imperial College London
Revisiting Thinning Methods for Kernel Learning Problems
Sep 7, 2026Blanca Cano-Camarero, Yago R. Aguado-Carrillo-de-Albornoz, Ángela Fernández-Pascual +1Kernel Methods
Departamento de Ingeniería Informática, Universidad Autónoma de Madrid, Madrid, Spain
Learning Kernels by Alignment for Multiclass Bayes Classification
Sep 6, 2026Hollan Haule, Javier EscuderoBayesian Inference Metric Learning
Geometry-Aware Graph Construction via Adaptive Spectral Bandwidth Control
Sep 3, 2026Ecem Bozkurt, Antonio OrtegaGraph Structure Learning Graph-Based Semi-Supervised Learning
Department of Electrical and Computer Engineering University of Southern California, Los Angeles, CA, USA

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Bilevel optimization for data-driven learning of Koopman embeddings using kernel-based autoencoders

Joel-Pascal Ntwali N'konzi, Feliks Nüske, Stefan Klus

Maxwell Institute for Mathematical Sciences, The University of Edinburgh & Heriot–Watt University, Edinburgh, United Kingdom · Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany · LAAS-CNRS, Université de Toulouse, Toulouse, France+1

Koopman Operator Learning Latent Dynamics Modeling Kernel Methods

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Koopman operator theory provides a linear framework for analyzing nonlinear dynamical systems and has become a major tool for data-driven modeling. A central challenge, however, is that finite-dimensional approximations computed by methods such as extended dynamic mode decomposition (EDMD) require the dictionary to be specified a priori. Recent machine-learning approaches address this limitation by learning the dictionary from data, predominantly using artificial neural network (ANN) autoencoder architectures. Although kernel methods offer an alternative with greater interpretability and tractability for theoretical analysis, they have received little attention in this setting. We introduce extended dynamic mode decomposition with kernel-based dictionary learning (EDMD-kDL), a kernel-based method for learning finite-dimensional Koopman embeddings directly from data. The method combines ideas from collocation methods and bilevel optimization to simultaneously learn a kernel dictionary and the corresponding Koopman approximation. We evaluate EDMD-kDL against state-of-the-art ANN-based approaches on a range of numerical experiments, including global sea-surface-temperature forecasting and learning directly from video data. Across all tested settings, EDMD-kDL achieves performance comparable to or better than the ANN-based methods. Moreover, in contrast to standard kernel methods, the proposed approach is scalable to large datasets by design since the size of the required kernel matrices depends on the number of collocation points rather than the size of the training dataset.

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Latest papers 168